112 results on '"Milan, Anton"'
Search Results
2. MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
- Author
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Dendorfer, Patrick, Ošep, Aljoša, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, Roth, Stefan, and Leal-Taixé, Laura
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light on potential future research directions., Comment: Accepted at IJCV
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- 2020
3. MOT20: A benchmark for multi object tracking in crowded scenes
- Author
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Dendorfer, Patrick, Rezatofighi, Hamid, Milan, Anton, Shi, Javen, Cremers, Daniel, Reid, Ian, Roth, Stefan, Schindler, Konrad, and Leal-Taixé, Laura
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios., Comment: The sequences of the new MOT20 benchmark were previously presented in the CVPR 2019 tracking challenge ( arXiv:1906.04567 ). The differences between the two challenges are: - New and corrected annotations - New sequences, as we had to crop and transform some old sequences to achieve higher quality in the annotations. - New baselines evaluations and different sets of public detections
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- 2020
4. Learn to Predict Sets Using Feed-Forward Neural Networks
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Rezatofighi, Hamid, Zhu, Tianyu, Kaskman, Roman, Motlagh, Farbod T., Shi, Qinfeng, Milan, Anton, Cremers, Daniel, Leal-Taixé, Laura, and Reid, Ian
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality. Depending on the problem under consideration, we define different training models for set prediction using deep neural networks. We demonstrate the validity of our set formulations on relevant vision problems such as: 1) multi-label image classification where we outperform the other competing methods on the PASCAL VOC and MS COCO datasets, 2) object detection, for which our formulation outperforms popular state-of-the-art detectors, and 3) a complex CAPTCHA test, where we observe that, surprisingly, our set-based network acquired the ability of mimicking arithmetics without any rules being coded., Comment: Accepted in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022. arXiv admin note: substantial text overlap with arXiv:1805.00613
- Published
- 2020
5. Comparison of the scientific performance in hip and knee arthroplasty between the leading continents
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Milan Anton Wolf, Philipp Winter, Stefan Landgraeber, and Patrick Orth
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hip ,knee ,arthroplasty ,comparative study ,continents ,research ,Surgery ,RD1-811 - Abstract
BackgroundScientific progress in the field of knee and hip arthroplasty has enabled the preservation of mobility and quality of life in the case of patients with many primary degenerative and (post-) traumatic joint diseases. This comparative study aims to investigate differences in scientific performance between the leading continents in the field of hip and knee arthroplasty.MethodsUsing specific search terms all studies published by the scientific leading continents Europe, North America, Asia and Oceania listed in the Web of Science databases were included. All identified publications were analysed and comparative conclusions were drawn regarding the qualitative and quantitative scientific merit of each continent.ResultsEurope, followed by North America, Asia, and Oceania, had the highest overall number of publications in the field of arthroplasty. Since 2000, there has been a strong increase in knee arthroplasty publication rate, particular pronounced in Asia. Studies performed and published in North America and those on knee arthroplasty received the highest number of fundings. Publications regarding hip arthroplasty achieved the highest average citation rate. In contradistinction to the others, in North America most funding was provided by private agencies.ConclusionAlthough Europe showed the highest total number of publications, authors and institutions, arthroplasty research from North America received greater scientific attention and financial support. Measured by citations, publications on hip arthroplasty attained higher scientific interest and studies on knee arthroplasty received higher economic affection.
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- 2023
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6. Subgroup analysis of scientific performance in the field of arthroplasty
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Milan Anton Wolf, Lars Goebel, Philipp Winter, Stefan Landgraeber, and Patrick Orth
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arthroplasty ,research patterns ,subgroup analysis ,bibliometric ,joint replacement ,Surgery ,RD1-811 - Abstract
IntroductionArthroplasty is the final treatment option for maintaining mobility and quality of life in many primary degenerative and (post-) traumatic joint diseases. Identification of research output and potential deficits for specific subspecialties may be an important measure to achieve long-term improvement of patient care in this field.MethodsUsing specific search terms and Boolean operators, all studies published since 1945 to the subgroups of arthroplasty listed in the Web of Science Core Collection were included. All identified publications were analysed according to bibliometric standards, and comparative conclusions were drawn regarding the scientific merit of each subgroup.ResultsMost publications investigated the subgroups of septic surgery and materials followed by approach, navigation, aseptic loosening, robotic and enhanced recovery after surgery (ERAS). In the last 5 years, research in the fields of robotic and ERAS achieved the highest relative increase in publications In contrast, research on aseptic loosening has continued to lose interest over the last 5 years. Publications on robotics and materials received the most funding on average while those on aseptic loosening received the least. Most publications originated from USA, Germany, and England, except for research on ERAS in which Denmark stood out. Relatively, publications on aseptic loosening received the most citations, whereas the absolute scientific interest was highest for the topic infection.DiscussionIn this bibliometric subgroup analysis, the primary scientific outputs focused on septic complications and materials research in the field of arthroplasty. With decreasing publication output and the least financial support, intensification of research on aseptic loosening is urgently recommended.
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- 2023
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7. CVPR19 Tracking and Detection Challenge: How crowded can it get?
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Dendorfer, Patrick, Rezatofighi, Hamid, Milan, Anton, Shi, Javen, Cremers, Daniel, Reid, Ian, Roth, Stefan, Schindler, Konrad, and Leal-Taixe, Laura
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16 and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios., Comment: arXiv admin note: substantial text overlap with arXiv:1603.00831, arXiv:1504.01942
- Published
- 2019
8. RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
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Schwarz, Max, Milan, Anton, Periyasamy, Arul Selvam, and Behnke, Sven
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic data sets possible. We evaluate our approach on two challenging data sets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task, and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception.
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- 2018
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9. Ultrasound‐guided determination demonstrates influence of age, sex and type of sport on medial femoral condyle cartilage thickness in children and adolescents
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Schneider, Dirk, primary, Weber, Regine, additional, Nourkami‐Tutdibi, Nasenien, additional, Bous, Michelle, additional, Goedicke‐Fritz, Sybelle, additional, Hans, Muriel Charlotte, additional, Hein, Steve, additional, Wolf, Milan Anton, additional, Landgraeber, Stefan, additional, Zemlin, Michael, additional, and Kaiser, Elisabeth, additional
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- 2024
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10. COVID-19: a catalyst for the digitization of surgical teaching at a German University Hospital
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Wolf, Milan Anton, Pizanis, Antonius, Fischer, Gerrit, Langer, Frank, Scherber, Philipp, Stutz, Janine, Orth, Marcel, Pohlemann, Tim, and Fritz, Tobias
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- 2022
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11. PoseTrack: A Benchmark for Human Pose Estimation and Tracking
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Andriluka, Mykhaylo, Iqbal, Umar, Insafutdinov, Eldar, Pishchulin, Leonid, Milan, Anton, Gall, Juergen, and Schiele, Bernt
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variety of tasks such as activity recognition, content retrieval and social signal processing. In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis. The benchmark encompasses three competition tracks focusing on i) single-frame multi-person pose estimation, ii) multi-person pose estimation in videos, and iii) multi-person articulated tracking. To facilitate the benchmark and challenge we collect, annotate and release a new %large-scale benchmark dataset that features videos with multiple people labeled with person tracks and articulated pose. A centralized evaluation server is provided to allow participants to evaluate on a held-out test set. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods. The benchmark is freely accessible at https://posetrack.net., Comment: www.posetrack.net
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- 2017
12. Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place
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McTaggart, M., Morrison, D., Tow, A. W., Smith, R., Kelly-Boxall, Norton, Milan, Anton, Zhuang, T. Pham Zheyu, Leitner, J., Reid, I., Corke, P., and Lehnert, C.
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Computer Science - Robotics - Abstract
Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have direct applications from automating warehouses to harvesting fruit in agriculture. One of the main challenges associated with these difficult robotic manipulation tasks is the motion planning and control problem for multi-DoF (Degree of Freedom) manipulators. This paper presents the design and performance evaluation of a low-cost Cartesian manipulator, Cartman who took first place in the Amazon Robotics Challenge 2017. It can perform pick and place tasks of household items in a cluttered environment. The robot is capable of linear speeds of 1 m/s and angular speeds of 1.5 rad/s, capable of sub-millimetre static accuracy and safe payload capacity of 2kg. Cartman can be produced for under 10 000 AUD. The complete design is open sourced and can be found at http://juxi.net/projects/AmazonRoboticsChallenge., Comment: ACRV Tech Report
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- 2017
13. Joint Learning of Set Cardinality and State Distribution
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Rezatofighi, S. Hamid, Milan, Anton, Shi, Qinfeng, Dick, Anthony, and Reid, Ian
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural language processing and other related problems. Despite their success, traditional architectures suffer from a serious limitation in that they are built to deal with structured input and output data, i.e. vectors or matrices. Many real-world problems, however, are naturally described as sets, rather than vectors. Existing techniques that allow for sequential data, such as recurrent neural networks, typically heavily depend on the input and output order and do not guarantee a valid solution. Here, we derive in a principled way, a mathematical formulation for set prediction where the output is permutation invariant. In particular, our approach jointly learns both the cardinality and the state distribution of the target set. We demonstrate the validity of our method on the task of multi-label image classification and achieve a new state of the art on the PASCAL VOC and MS COCO datasets., Comment: Accepted in AAAI 2018. arXiv admin note: text overlap with arXiv:1611.08998
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- 2017
14. COVID-19: a catalyst for the digitization of surgical teaching at a German University Hospital
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Milan Anton Wolf, Antonius Pizanis, Gerrit Fischer, Frank Langer, Philipp Scherber, Janine Stutz, Marcel Orth, Tim Pohlemann, and Tobias Fritz
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Surgical education ,eLearning ,Covid-19 ,Webinar ,Digitalization ,Special aspects of education ,LC8-6691 ,Medicine - Abstract
Abstract Background The summer semester 2020, had to be restructured due to the SARS-CoV-2 pandemic and the associated contact restrictions. Here, for the first time, the established lectures in lecture halls and small group seminars could not be conducted in presence as usual. A possible tool for the implementation of medical teaching, offers the use of eLearning, online webinars and learning platforms. At present it is unclear how the SARS-CoV-2 pandemic will affect surgical teaching, how digitization will be accepted by students, and how virtual teaching can be expanded in the future. Methods The teaching, which was previously delivered purely through face-to-face lectures, was completely converted to digital media. For this purpose, all lectures were recorded and were available to students on demand. The seminars were held as a twice a week occurring online webinar. The block internship was also conducted as a daily online webinar and concluded with an online exam at the end. At the end of the semester, a survey of the students was carried out, which was answered by n = 192 students with an anonymized questionnaire. The questionnaire inquires about the previous and current experience with eLearning, as well as the possibility of a further development towards a purely digital university. Results There were n = 192 students in the study population. For 88%, the conversion of classes to web-based lectures represented their first eLearning experience. For 77% of all students, the digitization of teaching led to a change in the way they prepare for class. 73% of the participating students are of the opinion that eLearning lectures should continue to be offered. 54% of the students felt that eLearning lectures made more sense than face-to-face lectures. A purely virtual university could be imagined by 41% of the students. Conclusion The conversion of teaching represented the first contact with eLearning for most students. Overall, the eLearning offering was experienced as positive. Due to the new teaching structure, the way of learning had already changed during the semester. Based on the new eLearning content, the already existing formats can be further expanded in the future. Nevertheless, it turned out that the practical-surgical contents and skills cannot be adequately represented by purely online offers; for this, the development of hybrid practice-oriented teaching concepts is necessary.
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- 2022
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15. Electrochemical DNA biosensor coupled to LAMP reaction for early diagnostics of cervical precancerous lesions
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Ravery Sebuyoya, Ludmila Moranova, Nasim Izadi, Lukas Moran, Roman Hrstka, Milan Anton, and Martin Bartosik
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Electrochemistry ,HPV ,Screen-printed electrode ,DNA biosensor ,Loop-mediated isothermal amplification ,Cervical cancer ,Biotechnology ,TP248.13-248.65 - Abstract
Electrochemical (EC) biosensors are potentially useful tools for detection of DNA and RNA cancer biomarkers. Gold screen-printed electrodes (AuSPEs) are especially popular due to their easy manufacturing and surface modifications, making them useful candidates in biosensor fabrication. On the other hand, biosensors based on AuSPEs are still not often applied to clinical samples from cancer patients or compared with routine laboratory methods. Therefore, we tried to address these challenges by developing AuSPE-based biosensor for detection of DNA from two highly oncogenic types of human papillomavirus (HPV), HPV16 and HPV18, that cause cervical cancer in women. We coupled loop-mediated isothermal amplification (LAMP) of HPV DNA from fifteen clinical samples, with the capture of amplified products at the surface of the AuSPE and with final EC detection in a multielectrode format. We show excellent selectivity of designed primers and probes by comparing HPV-positive and HPV-negative cancer cell lines, and most importantly, very good sensitivity and specificity of the proposed biosensor for detection of clinical samples when compared to the PCR used as a gold standard.
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- 2022
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16. Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
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Leal-Taixé, Laura, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, and Roth, Stefan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.
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- 2017
17. DeepSetNet: Predicting Sets with Deep Neural Networks
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Rezatofighi, S. Hamid, G, Vijay Kumar B, Milan, Anton, Abbasnejad, Ehsan, Dick, Anthony, and Reid, Ian
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Learning - Abstract
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets., Comment: Accepted in IEEE International Conference on Computer Vision (ICCV), Venice, 2017, (Spotlight)
- Published
- 2016
18. PoseTrack: Joint Multi-Person Pose Estimation and Tracking
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Iqbal, Umar, Milan, Anton, and Gall, Juergen
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for multi-person pose estimation in images cannot be applied directly to this problem, since it also requires to solve the problem of person association over time in addition to the pose estimation for each person. We therefore propose a novel method that jointly models multi-person pose estimation and tracking in a single formulation. To this end, we represent body joint detections in a video by a spatio-temporal graph and solve an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. The proposed approach implicitly handles occlusion and truncation of persons. Since the problem has not been addressed quantitatively in the literature, we introduce a challenging "Multi-Person PoseTrack" dataset, and also propose a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons. Finally, we evaluate the proposed approach and several baseline methods on our new dataset., Comment: Accepted to CVPR 2017
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- 2016
19. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
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Lin, Guosheng, Milan, Anton, Shen, Chunhua, and Reid, Ian
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.
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- 2016
20. Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017
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Leitner, Jürgen, Morrison, Douglas, Milan, Anton, Kelly-Boxall, Norton, McTaggart, Matthew, Tow, Adam W., Corke, Peter, Causo, Albert, editor, Durham, Joseph, editor, Hauser, Kris, editor, Okada, Kei, editor, and Rodriguez, Alberto, editor
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- 2020
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21. Online Multi-Target Tracking Using Recurrent Neural Networks
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Milan, Anton, Rezatofighi, Seyed Hamid, Dick, Anthony, Reid, Ian, and Schindler, Konrad
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.
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- 2016
22. MOT16: A Benchmark for Multi-Object Tracking
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Milan, Anton, Leal-Taixe, Laura, Reid, Ian, Roth, Stefan, and Schindler, Konrad
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest., Comment: arXiv admin note: substantial text overlap with arXiv:1504.01942
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- 2016
23. Multiple object tracking: A literature review
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Luo, Wenhan, Xing, Junliang, Milan, Anton, Zhang, Xiaoqin, Liu, Wei, and Kim, Tae-Kyun
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- 2021
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24. MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
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Dendorfer, Patrick, Os̆ep, Aljos̆a, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, Roth, Stefan, and Leal-Taixé, Laura
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- 2021
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25. MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
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Leal-Taixé, Laura, Milan, Anton, Reid, Ian, Roth, Stefan, and Schindler, Konrad
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking.
- Published
- 2015
26. Multiple Object Tracking: A Literature Review
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Luo, Wenhan, Xing, Junliang, Milan, Anton, Zhang, Xiaoqin, Liu, Wei, and Kim, Tae-Kyun
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Computer Science - Computer Vision and Pattern Recognition ,I.4.8 - Abstract
Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent advances in various aspects and propose some interesting directions for future research. To the best of our knowledge, there has not been any extensive review on this topic in the community. We endeavor to provide a thorough review on the development of this problem in recent decades. The main contributions of this review are fourfold: 1) Key aspects in an MOT system, including formulation, categorization, key principles, evaluation of MOT are discussed; 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks; 3) We examine experiments of existing publications and summarize results on popular datasets to provide quantitative and comprehensive comparisons. By analyzing the results from different perspectives, we have verified some basic agreements in the field; and 4) We provide a discussion about issues of MOT research, as well as some interesting directions which will become potential research effort in the future., Comment: Accepted by Artificial Intelligence
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- 2014
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27. Comparison of the scientific performance in hip and knee arthroplasty between the leading continents
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Wolf, Milan Anton, primary, Winter, Philipp, additional, Landgraeber, Stefan, additional, and Orth, Patrick, additional
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- 2023
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28. Subgroup analysis of scientific performance in the field of arthroplasty
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Wolf, Milan Anton, primary, Goebel, Lars, additional, Winter, Philipp, additional, Landgraeber, Stefan, additional, and Orth, Patrick, additional
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- 2023
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29. Digital tools in musculoskeletal rehabilitation
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Wolf, Milan Anton, primary, Kosmalla, Felix, additional, and Landgraeber, Stefan, additional
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- 2023
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30. Subgroup analysis of scientific performance in the field of arthroplasty
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Wolf, Milan Anton, Goebel, Lars, Winter, Philipp, Landgraeber, Stefan, and Orth, Patrick
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joint replacement ,bibliometric ,research patterns ,arthroplasty ,subgroup analysis - Published
- 2023
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31. New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers
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Jitka Holcakova, Martin Bartosik, Milan Anton, Lubos Minar, Jitka Hausnerova, Marketa Bednarikova, Vit Weinberger, and Roman Hrstka
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cervical cancer ,endometrial cancer ,ovarian cancer ,precancer ,liquid biopsy ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.
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- 2021
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32. Privacy Preserving Multi-target Tracking
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Milan, Anton, Roth, Stefan, Schindler, Konrad, Kudo, Mineichi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jawahar, C. V., editor, and Shan, Shiguang, editor
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- 2015
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33. Improving Global Multi-target Tracking with Local Updates
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Milan, Anton, Gade, Rikke, Dick, Anthony, Moeslund, Thomas B., Reid, Ian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Agapito, Lourdes, editor, Bronstein, Michael M., editor, and Rother, Carsten, editor
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- 2015
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34. Learn to Predict Sets Using Feed-Forward Neural Networks
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Rezatofighi, Hamid, primary, Zhu, Tianyu, additional, Kaskman, Roman, additional, Motlagh, Farbod T., additional, Shi, Javen Qinfeng, additional, Milan, Anton, additional, Cremers, Daniel, additional, Leal-Taixe, Laura, additional, and Reid, Ian, additional
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- 2022
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35. Virtual Surgical Training – first experiences of an online suture course
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Stutz, Janine, primary, Pizanis, Antonius, additional, Wolf, Milan Anton, additional, Fischer, Gerrit, additional, Langer, Frank, additional, Scherber, Philipp-Robert, additional, Orth, Marcel, additional, Pohlemann, Tim, additional, and Fritz, Tobias, additional
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- 2022
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36. 2022-RA-900-ESGO Performance of a LAMP-based electrochemical bioassay for determination of high-risk HPV infection in clinical settings
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Milan Anton, Ludmila Moráňová, Roman Hrstka, Martin Bartošík, and Luboš Minář
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- 2022
37. Virtual Surgical Training – first experiences of an online suture course
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Janine Stutz, Antonius Pizanis, Milan Anton Wolf, Gerrit Fischer, Frank Langer, Philipp-Robert Scherber, Marcel Orth, Tim Pohlemann, and Tobias Fritz
- Abstract
Background: Due to the SARS-CoV-2-pandemic and associated contact restrictions, established lectures and seminars could not take place in presence as usual in 2020. In the first online-based summer semester 2020, the lack of practical content was noted by medical students of our institution. To provide basic practical surgical skills, a hybrid course was developped. A home-based suture kit was given to all medical students and a web-based live suturing skills course was performed. Methods: A total of 104 medical students participated in the online suturing skills course in 2 remote video sessions (Microsoft Teams®, Redmond, USA) of 90 minutes each. The students were taught theoretical and practical contents of surgical suturing skills and knotting techniques by a surgical resident in small groups of 12 participants. For this purpose, each student was previously given a personal basic surgical instruments training kit. Single stitches, mattress suture, intracutaneous suture and surgical hand knots were trained. In addition, an online questionnaire (Survey Monkey), which was processed before and after the course was performed. Furthermore, the evaluation of surgical skills, the course execution and the intended future medical discipline were investigated via the survey. Results and conclusion: All students rated increased skills of their suture and knot skills after the course. 80% of them had no experience with basic surgical skills prior to the course. Before the course, 33% of the participants wanted to become a surgeon compared to 41% after the course. The skills to perform single stitches and a surgical hand knot increased by 70% after the course. Also, theoretical knowledge increased after the course. Furthermore, the students were satisfied with the concept and the implementation of the course. In summary, by means of this online suturing skills course without any personal contact between instructor and student the competence of medical students in basic surgical skills could be improved and students were motivated to pursue a career in surgery. This hybrid format appears as a viable alternative, therefore it should be integrated into the curriculum in addition to courses in presence.
- Published
- 2022
38. Application of an electrochemical LAMP-based assay for screening of HPV16/HPV18 infection in cervical samples
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Milan Anton, Ludmila Moranova, Roman Hrstka, and Martin Bartošík
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Cervical cancer ,0303 health sciences ,business.industry ,viruses ,General Chemical Engineering ,Concordance ,General Engineering ,Gold standard (test) ,medicine.disease ,Virology ,3. Good health ,Analytical Chemistry ,law.invention ,03 medical and health sciences ,Hpv testing ,0302 clinical medicine ,law ,030220 oncology & carcinogenesis ,Positive predicative value ,Genotype ,medicine ,Hpv test ,business ,Polymerase chain reaction ,030304 developmental biology - Abstract
Cervical cancer is caused by a persistent infection with high-risk types of human papillomaviruses (HPVs), especially HPV16 and HPV18. HPV testing therefore represents an important alternative to standard cytological screening. However, existing HPV tests are often laborious, expensive or time-consuming, and thus novel techniques are developed that would be inexpensive, rapid, easy to perform and could be implemented in low-resource settings. In this work, we show an early application of an electrochemical (EC) assay combined with loop-mediated amplification (LAMP) to determine HPV16 and HPV18 status directly in a clinical material. To demonstrate its potential usefulness, we screened 61 cervical samples for the presence of both genotypes, and compared the results with the gold standard – PCR amplification followed by gel electrophoresis and two commercial HPV tests – COBAS and INNO-LiPA. The best concordance was obtained with the PCR reaction; we obtained high significance (p-value
- Published
- 2020
39. Corona und die chirurgische Lehre: Erfahrung, Akzeptanz und zukünftige Aspekte des eLearning
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Wolf, Milan Anton, Stutz, Janine, Scherber, Philipp, Fischer, Gerrit, Langer, Frank, Pizanis, Antonius, Pohlemann, Tim, and Fritz, Tobias
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Digitalisierung ,ddc: 610 ,Lehre ,Medicine and health ,Corona ,eLearning - Abstract
Fragestellung: Das Sommersemester 2020, musste aufgrund der SARS-CoV-2-Pandemie und den damit verbundenen Kontaktbeschränkungen neu strukturiert werden. Hierbei konnten erstmalig im Sommersemester 2020 die etablierten Lehrveranstaltungen in Hörsälen und kleingruppenbezogenen Seminaren [zum vollständigen Text gelangen Sie über die oben angegebene URL]
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- 2021
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40. Electrochemical bioassay coupled to LAMP reaction for determination of high-risk HPV infection in crude lysates
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Martin Bartošík, Nasim Izadi, Ludmila Moranova, Milan Anton, Roman Hrstka, and Ravery Sebuyoya
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Loop-mediated isothermal amplification ,01 natural sciences ,Biochemistry ,Analytical Chemistry ,03 medical and health sciences ,chemistry.chemical_compound ,Genotype ,medicine ,Environmental Chemistry ,Bioassay ,Humans ,Papillomaviridae ,Spectroscopy ,030304 developmental biology ,Cervical cancer ,0303 health sciences ,Human papillomavirus 18 ,Chemistry ,010401 analytical chemistry ,Papillomavirus Infections ,medicine.disease ,DNA extraction ,Molecular biology ,3. Good health ,0104 chemical sciences ,High risk hpv ,Biological Assay ,Female ,Nucleic Acid Amplification Techniques ,DNA ,Homogenization (biology) - Abstract
Electrochemical (EC) detection of DNA biomarkers represents an interesting tool in molecular oncology due to its sensitivity, simplicity, low cost or rapid times of measurement. However, majority of EC assays, same as most optical-based techniques, require preceding DNA extraction step to remove other cellular components, making these assays more laborious and time-consuming. One option to circumvent this is to use LAMP (loop-mediated amplification), an isothermal amplification technique that can amplify DNA directly in crude lysates in a short time at a constant temperature. Here, we coupled the LAMP reaction with EC readout to detect DNA from the two most common oncogenic human papillomavirus (HPV) types that cause cervical cancer in women, i.e. HPV 16 and HPV 18, directly in crude lysates without a need for DNA extraction step. We show that in crude lysates, the LAMP reaction was superior to PCR, with very good selectivity on a panel of cancer cell lines and with high sensitivity, enabling detection of HPV DNA from as few as 10 cells. As a proof of principle, we applied the assay to nineteen clinical samples both from uninfected women and from women suffering from cervical precancerous lesions caused by HPV 16 or HPV 18 genotypes. Clinical samples were simply boiled for 5 min in homogenization buffer without DNA extraction step, and amplified with LAMP. We obtained excellent concordance of our assay with PCR, reaching 100% sensitivity for both genotypes, 81.82% specificity for HPV 16 and 94.12% specificity for HPV 18. Proposed assay could be a straightforward, simple, rapid and sensitive alternative for early diagnostics of precancerous cervical lesions.
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- 2021
41. Improving Global Multi-target Tracking with Local Updates
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Milan, Anton, primary, Gade, Rikke, additional, Dick, Anthony, additional, Moeslund, Thomas B., additional, and Reid, Ian, additional
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- 2015
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42. Privacy Preserving Multi-target Tracking
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Milan, Anton, primary, Roth, Stefan, additional, Schindler, Konrad, additional, and Kudo, Mineichi, additional
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- 2015
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43. The role of miR-409-3p in regulation of HPV16/18-E6 mRNA in human cervical high-grade squamous intraepithelial lesions
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Milan Anton, Roman Hrstka, Borivoj Vojtesek, Pavla Bouchalová, Hedvika Jasickova, Eva Jandáková, Iveta Selingerová, Lucia Sommerova, Martin Bartošík, and Vladimir Rak
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0301 basic medicine ,Squamous Intraepithelial Lesions ,030106 microbiology ,Down-Regulation ,Uterine Cervical Neoplasms ,medicine.disease_cause ,law.invention ,03 medical and health sciences ,Downregulation and upregulation ,law ,Cell Line, Tumor ,Virology ,microRNA ,medicine ,Humans ,Pharmacology ,Cervical cancer ,Human papillomavirus 16 ,Human papillomavirus 18 ,Oncogene ,business.industry ,Gene Expression Profiling ,Oncogene Proteins, Viral ,medicine.disease ,Epithelium ,Up-Regulation ,3. Good health ,Gene Expression Regulation, Neoplastic ,Repressor Proteins ,MicroRNAs ,030104 developmental biology ,medicine.anatomical_structure ,Dysplasia ,Cancer research ,Suppressor ,Female ,business ,Carcinogenesis - Abstract
Cervical cancer is one of the most common malignancies in women. MicroRNAs (miRNAs) are involved in a variety of fundamental cellular processes, including carcinogenesis. The potential utilization of aberrantly expressed miRNAs as novel biomarkers in cervical cancer diagnostics is growing. We investigated miRNA expression profiles during the progression of dysplasia in cervical epithelium to identify aberrantly expressed miRNAs. High-throughput miRNA profiling of high-grade precancerous lesions identified 79 miRNAs showing significant difference in expression values compared to normal cervical epithelium. Ten selected miRNAs were subsequently measured in an independent group of samples to validate them as promising biomarkers of cervical carcinogenesis. MicroRNAs miR-10b-5p, miR-34c-5p, miR-409-3p and miR-411-5p were confirmed as downregulated, while miR-10a-5p, miR-132-3p, miR-141-5p were significantly upregulated in dysplastic cervical tissues. Further investigation revealed an inverse correlation of miR-409-3p with E6 mRNA levels in precancerous cervical lesions. Subsequent in vitro analyses showed a direct involvement of this miRNA in the regulation of E6 oncogene levels, thus confirming a potential tumor suppressor function of miR-409-3p in cervical malignancies. Hence, miR-409-3p may represent a useful early marker and a potential therapeutic target for cervical cancer.
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- 2019
44. MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
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Dendorfer, Patrick, primary, Os̆ep, Aljos̆a, additional, Milan, Anton, additional, Schindler, Konrad, additional, Cremers, Daniel, additional, Reid, Ian, additional, Roth, Stefan, additional, and Leal-Taixé, Laura, additional
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- 2020
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45. Electrochemical chip-based genomagnetic assay for detection of high-risk human papillomavirus DNA
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Borivoj Vojtesek, Roman Hrstka, Milan Anton, Eva Jandáková, Martin Bartošík, and Helena Durikova
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Biomedical Engineering ,Biophysics ,Uterine Cervical Neoplasms ,Biosensing Techniques ,Cervix Uteri ,02 engineering and technology ,Biology ,01 natural sciences ,DNA sequencing ,Cell Line, Tumor ,Electrochemistry ,Human papillomavirus DNA ,medicine ,Humans ,Papillomaviridae ,Cervical cancer ,Human papillomavirus 16 ,Human papillomavirus 18 ,Hybridization probe ,Papillomavirus Infections ,010401 analytical chemistry ,High mortality ,Reproducibility of Results ,Electrochemical Techniques ,General Medicine ,Standard methods ,021001 nanoscience & nanotechnology ,medicine.disease ,Cervical smears ,Molecular biology ,3. Good health ,0104 chemical sciences ,Hpv testing ,DNA, Viral ,Cancer research ,Female ,0210 nano-technology ,Biotechnology - Abstract
Cervical cancer, being the fourth leading cause of cancer death in women worldwide, predominantly originates from a persistent infection with a high-risk human papillomavirus (HPV). Detection of DNA sequences from these high-risk strains, mostly HPV-16 and HPV-18, represents promising strategy for early screening, which would help to identify women with higher risk of cervical cancer. In developing countries, inadequate screening options lead to disproportionately high mortality rates, making a fast and inexpensive detection schemes highly important. Electrochemical sensors and assays offer an alternative to current methods of detection. We developed an electrochemical-chip based assay, in which target HPV DNA is captured via magnetic bead-modified DNA probes, followed by an antidigoxigenin-peroxidase detection system at screen-printed carbon electrode chips, enabling parallel measurements of eight samples simultaneously. We show sensitive detection in attomoles of HPV DNA, selective discrimination between HPV-16 and HPV-18 and good reproducibility. Most importantly, we show application of the assay into both cancer cell lines and cervical smears from patients. The electrochemical results correlated well with standard methods, making this assay potentially applicable in clinical practice.
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- 2016
46. Expression and Functional Characterization of miR-34c in Cervical Cancer
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Milan Anton, Lucia Sommerova, Eva Jandáková, Roman Hrstka, Bořivoj Vojtěšek, and Hedvika Fraňková
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Regulation of gene expression ,Cervical cancer ,Cell growth ,Uterine Cervical Neoplasms ,Cancer ,Biology ,medicine.disease ,Epithelium ,law.invention ,Blot ,MicroRNAs ,Oncology ,Downregulation and upregulation ,law ,Cell Line, Tumor ,microRNA ,Biomarkers, Tumor ,Cancer research ,medicine ,Humans ,Female ,Polymerase chain reaction ,Cell Proliferation - Abstract
Background Cervical cancer is the fourth most common cancer in women and is usually associated with human papillomavirus infection. Viral infections are usually characterized by morphological changes of epithelial cells; however, it is difficult to determine using recently available screening methods whether the changes are caused by productive infection or by malignant disease. Thus, new efforts are required to find novel diagnostic biomarkers of cervical cancer, such as miRNAs, which are small non-coding RNAs involved in the regulation of gene expression. Materials and methods miR-34c levels in cervical cancer cell lines were determined by the droplet-digital polymerase chain reaction. Changes in miR-34c expression in vitro were achieved by transient transfection with a specific miRNA mimic and inhibitor oligonucleotides. Cell proliferation was analyzed by crystal violet staining followed by spectrophotometric measurements. The effect on migratory properties was studied using a „scratch“ assay. Western blotting analysis was used to determine the expression of selected proteins. Results The downregulation of miR-34c expression was associated with a slight increase in cellular proliferation and a significant increase in cell migration. The analysis of miR-34c expression performed on a set of 39 dysplastic tissues and 35 samples of healthy controls subsequently revealed a significant difference (p l 0.01) in the level of this miRNA. Conclusion Comparative expression analysis revealed lower expression of miR-34c in cervical precanceroses than in normal untransformed epithelium. in vitro modulation of miR-34c expression revealed its tumor suppressor role in cervical malignancies. Key words: cervical cancer - HPV - miRNA - HSIL - hsa-miR-34c - precancerosis This work was supported by the projects MEYS - NPS I - LO1413, P206/12/G151 and MH CZ - DRO (MMCI, 00209805). The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers. Accepted: 16. 7. 2018.
- Published
- 2018
47. Genomagnetic LAMP-based electrochemical test for determination of high-risk HPV16 and HPV18 in clinical samples
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Ludmila Jiráková, Roman Hrstka, Martin Bartošík, Milan Anton, and Borivoj Vojtesek
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02 engineering and technology ,01 natural sciences ,Biochemistry ,Analytical Chemistry ,Cell Line, Tumor ,medicine ,Environmental Chemistry ,Humans ,Pap test ,Parallel detection ,Electrodes ,Spectroscopy ,Cervical cancer ,Human papillomavirus 16 ,medicine.diagnostic_test ,Hpv types ,Human papillomavirus 18 ,Chemistry ,010401 analytical chemistry ,virus diseases ,Electrochemical Techniques ,021001 nanoscience & nanotechnology ,medicine.disease ,female genital diseases and pregnancy complications ,Amperometry ,3. Good health ,0104 chemical sciences ,Hpv testing ,Cancer research ,Cancer cell lines ,0210 nano-technology ,Nucleic Acid Amplification Techniques - Abstract
Major cause of cervical cancer is a persistent infection with high-risk types of human papillomaviruses (HPV). For that reason, HPV testing is now becoming an important addition to standard cytological screening of cervical malignancies in women (known as Pap test). New methods are sought which could offer rapid and inexpensive detection schemes, such as those based on electrochemical (EC) readout. Here, we developed an assay for parallel detection of two most oncogenic high-risk HPV types, HPV16 and HPV18, by combining loop-mediated amplification (LAMP) of template DNA, its separation using magnetic beads and detection with amperometry at carbon-based electrode chips. Our EC-LAMP test enabled us to successfully discriminate both HPV types not only in cancer cell lines, but also using clinical material obtained from HPV-positive patient samples.
- Published
- 2018
48. RefineNet: Multi-Path Refinement Networks for Dense Prediction
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Lin, Guosheng, primary, Liu, Fayao, additional, Milan, Anton, additional, Shen, Chunhua, additional, and Reid, Ian, additional
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- 2019
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49. Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
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Leal-Taix��, Laura, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, and Roth, Stefan
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.
- Published
- 2017
50. RefineNet: Multi-Path Refinement Networks for Dense Prediction.
- Author
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Lin, Guosheng, Liu, Fayao, Milan, Anton, Shen, Chunhua, and Reid, Ian
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CONVOLUTIONAL neural networks ,FORECASTING ,OBJECT recognition (Computer vision) ,MATHEMATICAL convolutions - Abstract
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense prediction problems such as semantic segmentation and depth estimation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments on semantic segmentation which is a dense classification problem and achieve good performance on seven public datasets. We further apply our method for depth estimation and demonstrate the effectiveness of our method on dense regression problems. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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