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The technology of processors has advanced significantly, resulting in smaller and more powerful devices with much processing capability. Particularly, camera technology has witnessed extensive research in utilizing images for various applications. Currently, surveillance cameras are widely used for security purposes when abnormal events occur. In this research, the benefits of utilizing data from surveillance cameras are explored to assist in determining the position of a moving robot using the perspective-n-point (PnP) technique. the scale factor, which varies, has been improved by integrating checks with the YOLOv5 algorithm. This algorithm employs a custom model to specifically detect the robot of interest, enabling the determination of its real-world position using multiple surveillance cameras. These cameras have different perspectives within the same area. Considering the deviation caused by determining the position from a single viewpoint, multiple cameras are employed to mitigate this issue. [ABSTRACT FROM AUTHOR] more...

Cameras are increasingly used by ecologists to study species distribution and interactions. They are mainly used to study large animals such as mammals but can also be used to record small invertebrates, including insects.Camera traps, capturing images within a specified field of view, can be used for biomonitoring and investigating insect‐related interactions, such as predation. Understanding predation on insect prey has direct implications for agriculture and conservation biology, enabling predator species identification and quantification of biological control.This review examines 28 studies published between 1988 and March 2024 focusing on the use of cameras to monitor insect predator–prey interactions, predominantly targeting agricultural pests. Studies varied in recording equipment used and tended to be spatially and temporally limited, making results difficult to generalise at larger scale.We provide an overview of equipment options, camera settings, the merits of video versus picture recording, night‐time imaging strategies, trigger mechanisms, equipment costs, and strategies for managing theft and vandalism. Additionally, we discuss avenues for improving image processing efficiency, including enhancing predator identification through artificial intelligence methods. Challenges related to limitations in the taxonomic levels of predator identification are also addressed.Finally, we offer guidelines for researchers interested in using camera technology and propose future perspectives on their use in insect conservation and biocontrol efforts. [ABSTRACT FROM AUTHOR] more...

Urban flooding has become increasingly frequent due to the rising intensity of rainfall driven by urban development and climate change. Effective prevention measures are crucial to mitigate the significant human and material damages caused by such events. Rapid and accurate pre-detection techniques can help to reduce the impacts of urban flooding. With the advancement of deep learning, deep neural networks (DNNs) have been successfully applied across various domains, including computer vision and speech recognition. In particular, DNNs for computer vision demonstrate high performance with relatively low computational costs. In this paper, we propose a flooding region segmentation model for urban underpasses based on the U-Net architecture. To train and evaluate the model, we collected datasets from the Mannyeon, Oryang, and Daedong underpasses in Daejeon. The proposed method achieved Dice coefficients of 98.8%, 94.03%, and 93.85%, respectively. This model demonstrates high segmentation performance in detecting flooded regions and can be integrated into continuous flood monitoring systems. [ABSTRACT FROM AUTHOR] more...

Video injection attack is one of the risks associated with the use of surveillance cameras. Individuals can deceive authorities in a variety of ways when their faces are captured on camera monitoring technology. As a result, numerous types of techniques have been devised to identify counterfeit videos injected into mobile phone displays or in which faces have been substituted with photographs. Face-based video injection detection is investigated in this study by employing deep learning and machine learning techniques. There is one class of authentic face data, and five classes of fabricated face videos comprise the six classes of data in the set. Classification is accomplished using machine learning algorithms such as KNN, SVM, Random Forest and characteristics including texture, color, and shape. In contrast, deep learning does not perform the extraction of features, and optimized using CNN, ANN, and RNN algorithms. The experimental findings indicate that a convolutional neural network (CNN) achieves the highest level of accuracy 100%, followed by the ANN and RNN algorithms with an accuracy of 96% each, both with and without data augmentation. Furthermore, when applied to texture and color features, machine learning in the form of SVM and Random Forest achieved an accuracy of 99%, outperforming the KNN algorithm which had accuracy of 97%. These outcomes demonstrate that deep learning can generate better accurate predictions on a variety of data sets, especially with augmented dataset. [ABSTRACT FROM AUTHOR] more...

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more "intelligent" monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding. [ABSTRACT FROM AUTHOR] more...

The wide application of urban outdoor surveillance systems has greatly improved the efficiency of urban management and social security index. However, most of the existing urban outdoor surveillance cameras lack the records of important parameters such as geospatial coordinates, field of view angle and lens distortion, which brings difficulties to the unified management and layout optimization of the cameras, geospatial analysis of video data, and the computer vision applications such as the trajectory tracking of moving targets. To address this problem, this paper designs a marker with a chessboard pattern and a positioning device, makes the marker move in outdoor space through vehicles and other mobile carriers, and utilizes the marker image captured by the surveillance camera and the spatial position information obtained by the positioning device to batch calibrate the outdoor surveillance cameras and calculate its geospatial coordinates and field of view angle, which achieves the rapid acquisition of important parameters of the surveillance camera, and provides a new method for the rapid calibration of urban outdoor surveillance cameras, which contributes to the informationization management of urban surveillance resources and the spatial analysis and computation of surveillance video data, and make it play a greater role in the application of smart transportation and smart city. Taking the outdoor surveillance cameras within 2.5Km2 of a city as an example, calibration tests were performed on 295 surveillance cameras in the test area, and the geospatial coordinates, field of view angle and lens parameters of 269 surveillance cameras were obtained, and the average error of the spatial position was 0.527 m, and the maximum error was 1.573 m, and the average error of the field of view angle was 1.63°, and the maximum error was 3.4°, which verified the effectiveness and accuracy of the method in this paper. [ABSTRACT FROM AUTHOR] more...

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time. [ABSTRACT FROM AUTHOR] more...

Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia. [ABSTRACT FROM AUTHOR] more...

The 3D reconstruction of construction sites is of great importance for construction progress, quality, and safety management. Currently, most of the existing 3D reconstruction methods are unable to conduct continuous and uninterrupted perception, and it is difficult to achieve registration with real coordinates and dimensions. This study proposes a hierarchical registration framework for 3D reconstruction of construction sites based on surveillance cameras. This method can quickly perform on-site 3D reconstruction and restoration by taking surveillance camera images as inputs. It combines 2D and 3D features and does not need transfer learning or camera calibration. By experimenting on one construction site, we found that this framework can complete the 3D point cloud estimation and registration of construction sites within an average of 3.105 s through surveillance images. The average RMSE of the point cloud within the site is 0.358 m, which is better than most point cloud registration methods. Through this method, 3D data within the scope of surveillance cameras can be quickly obtained, and the connection between 2D and 3D can be effectively established. Combined with visual information, it is beneficial to the digital twin management of construction sites. more...

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant. [ABSTRACT FROM AUTHOR] more...

The current research sought to provide a sociological analysis of the repercussions of witnessed crimes, which were captured by surveillance cameras, smartphones, or live broadcast via the media and social media, on copycat crime and copycat criminals. This is for the purpose of answering a main question: What are the repercussions of photographing and publishing the crime witnessed via surveillance cameras, smartphones, or live broadcast on the commission of copycat crimes? From this standpoint, this study belongs to the type of analytical exploratory research The social sample survey approach was employed. To obtain results that reveal the reality of crimes witnessed in the commission of imitation crimes; This is through the scale, which consists of five axes, each of which includes a number of closed questions. (450) questionnaires from the scale were distributed to the research sample, and (415) were retrieved. The research results have shown that the increasing number of photos and videos being viewed about crimes committed in society; It is increasing with the consumption of social media, the Internet and satellite channels. The results also revealed that social media came in first place, while official media came in second place in terms of media outlets that circulate crime news. It also showed an increase in the responses of the research sample regarding the role of social media in encouraging individuals to imitate crimes that have occurred in society. A percentage (95.9%) of the total research sample reported the influence of social media in encouraging imitation and committing crimes by publishing them. The results also showed that among the types of witnessed crimes that were filmed by surveillance cameras, smart phones, or live broadcasts, and that the research sample witnessed, were murder crimes, which came in first place, and in second place came the Ismailia murder incident, and in third place came the Ismailia beating incident, and the Ismailia murder. The suicide of an engineering student from the Cairo Tower came in fourth place, harassment crimes came in fifth place, theft crimes came in sixth place, train accidents came in seventh place, terrorism crimes came in eighth place, kidnapping crimes came in ninth place, and parricide crimes came in eighth place. His children are ranked tenth and last. Finally, the results of the study revealed that among the effects of witnessed crimes that were filmed with surveillance cameras, smartphones, or live broadcast via Facebook, the effect of “belief in the spread of crimes in society” ranked first among the effects of filming witnessed crimes, and the effect of “belief in the spread of crimes in society” ranked second. “Individuals resort to criminal behavior to achieve their desires and goals,” the effect of “encouraging the imitation of crimes” came in third place, and in the third place came the effect of “the elaborate promotion of cultures, ideas, and customs that are subversive of morals and destroying values.” And in the fourth place came the effect of “repeated broadcasting.” Watching crimes makes the individual believe that they are normal, non-criminal matters, and the effect of “learning the arts of crime andknowing the methods and methods to which criminals resort” came in fifth place. [ABSTRACT FROM AUTHOR] more...

The construction sector is responsible for almost 30% of the world's total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people's thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people's heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems. [ABSTRACT FROM AUTHOR] more...

Copyright of Sociology & Technoscience / Sociología y Tecnociencia is the property of Universidad de Valladolid, Escuela Universitaria de Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

The traditional construction plan design of the surveillance camera is mainly based on previous experience using two-dimensional design method, it is unable to simulate the monitoring area of surveillance camera. When the terminal devices are obscured so that they cannot be pass security acceptance, it is necessary to increase the number of camera deployment, and results in decoration being demolished and leading to an increase in the construction period and construction cost. In order to solve the blind zone issue in surveillance camera, a method of analyzing the simulation of camera surveillance based on BIM technology was studied. The technique includes creating BIM model of the target building, deploying surveillance camera, determining the field of view area of the camera and reasonable surveillance area, analyzing the factors affecting the obstruction of objects around the surveillance camera, etc. By applying this technology, monitoring areas and blind zones could be marked out, and the distribution of camera monitoring ranges could be mapped to confirm that there was no blind zone. The technology simulated the monitoring range of surveillance cameras and analyzed the blind zones, and effectively solving the problem of the blind zones of surveillance camera and realizing the integration of BIM technology and the security monitoring profession. It realized the security requirements of 'without dead ends and complete coverage for monitoring', which improved the construction drawing design and construction quality of monitoring camera in urban rail transit projects, high-speed rail stations, aviation airports and shopping malls, reduced construction costs, and improved the safety management level of project operations. [ABSTRACT FROM AUTHOR] more...

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks. [ABSTRACT FROM AUTHOR] more...

Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption. [ABSTRACT FROM AUTHOR] more...

The black stork is an endangered species in many countries, due to its low adaptability to environmental changes and its sensitivity to anthropogenic disturbances. In Italy, the most recent report on the species' nesting sites lists only 36 pairs, of which 16 are in Basilicata, 7 are in Calabria, 4 are in Piedmont and Molise and 1 is in Campania. This study focuses for the first time on the behavior of a Ciconia nigra pair in the Basilicata region, where the species nests exclusively on cliffs rather than in trees, as is more frequent elsewhere. The video recordings were used to observe the species during the pre- and post-hatching periods and to refer to the 2012 nesting season, as in that year, the video recordings covered the entire reproductive period. In the pre-hatching phase, the activity for which most time is spent is brooding, which lasts on average 43′ in the morning and in evening and more than 49′ at midday. In the post-hatching phase, a large part of the recording period is spent on activities related to parental care. There were no moments of inactivity during this phase, the parents were frequently observed setting up the nest and preening themselves, while they were rarely seen in a huddled position. The alert activity was also very frequent, especially at midday. Our study has shown that the black stork, a shy and cautious species, may return to nest in increasing numbers given the development of the promising Lucanian nucleus, if attention is paid to the habitat quality and feeding areas, where human activities should be avoided unless absolutely necessary. [ABSTRACT FROM AUTHOR] more...

Abstract Background As the number of older people increases, so does the need for care. However, the workforce in eldercare cannot increase at the rate required to match the needs. Welfare technologies, such as surveillance cameras, can replace physical visits and be used at night to monitor older people in order to keep them safe, while not disturbing their sleep. The aim of the paper is to analyze obstacles and opportunities associated with implementation and use of surveillance cameras at nursing homes from the perspectives of the practitioners who use the technology, their working environment and the conditions of the older people with cognitive impairment who live in nursing homes. Methods Individual semi-structured interviews were conducted with the eldercare personnel at nursing homes to understand their experiences of implementation and use of surveillance cameras. The transcribed interviews were analyzed using qualitative content analysis. The consolidated criteria for reporting qualitative research (COREQ) was used as a guidance tool. Results The results show that the eldercare personnel experienced lack of adequate information, education and support related to using surveillance cameras. Several benefits are highlighted, such as better working environment and that the residents were not unnecessarily disturbed at night. However, the results also show that it is important to clarify that surveillance cameras cannot replace the human presence. Conclusions The conclusions from this study are the importance of prerequisites for implementation, and that using surveillance cameras contributed to improvements in the working environment at night and created possibilities to maintain security and integrity for older people living in nursing homes. more...

In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed. [ABSTRACT FROM AUTHOR] more...

In highway management, intelligent vehicle detection and counting are becoming increasingly important as an accurate estimation of traffic density on road congestion reduction. Traffic density estimation is affected by the difficulties of perspective distortion, size change, significant occlusion, and background interference in traffic images. To address the previous issues, this article develops a novel model that enhances the quality of estimating traffic density. The efficientNet fine-tuning architecture is used then, followed by the development of seven dilated convolutional layers to extract the deeper features in the images that maintain the output's resolution to generate a high-quality density map. Finally, the vehicle count will be calculated from the high-quality density map. The experimental results indicate that the suggested approach significantly enhances the accuracy of traffic density estimation compared to the existing ones. It achieves 5.23 as a mean absolute error (MAE) on the TRANCOS dataset. [ABSTRACT FROM AUTHOR] more...

Background: As the number of older people increases, so does the need for care. However, the workforce in eldercare cannot increase at the rate required to match the needs. Welfare technologies, such as surveillance cameras, can replace physical visits and be used at night to monitor older people in order to keep them safe, while not disturbing their sleep. The aim of the paper is to analyze obstacles and opportunities associated with implementation and use of surveillance cameras at nursing homes from the perspectives of the practitioners who use the technology, their working environment and the conditions of the older people with cognitive impairment who live in nursing homes. Methods: Individual semi-structured interviews were conducted with the eldercare personnel at nursing homes to understand their experiences of implementation and use of surveillance cameras. The transcribed interviews were analyzed using qualitative content analysis. The consolidated criteria for reporting qualitative research (COREQ) was used as a guidance tool. Results: The results show that the eldercare personnel experienced lack of adequate information, education and support related to using surveillance cameras. Several benefits are highlighted, such as better working environment and that the residents were not unnecessarily disturbed at night. However, the results also show that it is important to clarify that surveillance cameras cannot replace the human presence. Conclusions: The conclusions from this study are the importance of prerequisites for implementation, and that using surveillance cameras contributed to improvements in the working environment at night and created possibilities to maintain security and integrity for older people living in nursing homes. [ABSTRACT FROM AUTHOR] more...

Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method. more...

Surely surveillance cameras are certainly important in all aspects of life. We have become in an era where we need to use surveillance cameras everywhere, homes, schools, banks, hospitals, and companies, even in the general streets, to monitor everything that happens and follow the progress of those places with all safety by surveillance videos. However, the pervasiveness of surveillance cameras has become an issue for people’s privacy. This paper proposes a novel method for surveillance video privacy protection using block scrambling-based encryption and DCNN-based object detection. An object detection model based on DCNN You Only Look Once version 3 (YOLOv3) is used to detect the faces of the people. Then, the detected faces are scrambled using the fast block scrambling technique. Finally, the scrambled faces are encrypted using a secret key produced from a chaotic logistic map. The bounding boxes that output from the YOLOv3 are modified to include the entire edges of the detected faces to prevent any leaks of the sensitive regions. The simulation results and security analysis confirmed the proposed method’s effectiveness in protecting the surveillance videos’ privacy. more...

Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption. more...

Copyright of Journal of Field Ornithology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

SUMMARY: Anomaly detection in video systems has been popular over several years. It is still challenging to detect anomalies in a static object. To manage this objective, we focus on changes in the position of a stationary object in videos. In a normal scenario, the pixel values of the static object are fixed while in abnormal motion the fixed values change. We introduce a new concept to determine anomalies based on manual annotations in each video frame, only over a part of a static object in a frame such that it can be taken as a reference for the whole. Through color channel splitting we determine mask image, from which handcrafted features such as scratch area, perimeter, equivalent diameter and density are calculated. In the next step, we analyze frame‐wise changes in feature values using a linear regression model, feature values are constant when the object remains stationary while there is a rise or fall in values when an object changes location. We classify feature values through anomaly scores and thresholds. In this model, we are evaluating our proposed framework on 12 real‐time video datasets. Results are compared with existing techniques which are outperforming in terms of accuracy, mean square error and area under the curve. [ABSTRACT FROM AUTHOR] more...

Images, which are captured in the night, have the poor quality in comparison to day light. In surveillance cameras, because of weather and other constraint images have low brightness, low contrast, and high noise. We need night vision in various sectors like automobile industry, LOC patrolling, Civil Security, Prevent accident, etc. In this paper, we try to improve image quality by the improving contrast enhancement algorithm along with developed luminance range. In this research, which is an extension of earlier work, we use Principal Component Analysis (PCA) technique as it contains significant information of pixels. We compare daytime images with enhanced images in various environment and variables to get a good quality image. We use Contrast enhancement for brightness and contrast, bilateral filter for de-noise and edge prevention, which work more efficiently over other methods. [ABSTRACT FROM AUTHOR] more...

Automatic detection of dangerous situations in order to ensure the safety of residents is a new step in the development of video surveillance systems in cities. And dangerous situations are often caused by deviant behavior of people: robbery, brawl, vandalism and etc. But due to the strong variability of such scenes, their detection is a challenging problem, which still remains unresolved. The key to solving this problem is the recognition of fine-grained features and events of scenes and the application of knowledge management technologies. In this paper, three computer vision technologies for detecting people, tracking people and estimating three-dimensional human poses were integrated with the aim of recognizing the actions and interactions of people in three-dimensional space. For all technologies an open source implementations were used that showed high results in popular computer vision challenges. A dataset was also created using computer graphics to test the developed system, containing scenes of the interaction of people in the city, shot under different point of views. This dataset showed that additional teaching of the human pose estimation component to handle challenging poses of people and camera viewpoints is required. more...

This paper proposes a traffic monitoring system that detects, tracks, and classifies multiple vehicles on the road in real time using various digital image processing techniques and the process of machine learning based on a convolutional neural network (CNN). With this system, a video camera is installed on the road, and calibration is used to obtain the projection equation of the actual road on the image plane. Several image processing techniques, such as background modeling, background extraction, edge detection, and object tracking, are used to develop and implement a prototype system. The proposed system also uses a transfer learning process that is more efficient than starting CNN from scratch. This maximizes training efficiency and increases prediction accuracy in vehicle classification. Preliminary experimental results demonstrate that multiple vehicle tracking and classification are possible while calculating vehicle speed. The ultimate goal of this study is to develop a single digital video camera system with embedded machine learning process that can monitor and distinguish multiple vehicles simultaneously in multiple lanes. [ABSTRACT FROM AUTHOR] more...

Traffic congestion detection plays an important role for road management. However, it is difficult to automatically report traffic congestion when it occurs in large-scale road network. One of key challenges for rapidly and precisely identifying early congestion is huge variations in appearance caused by illumination, weather, camera settings and other traffic conditions. To address it, we proposed a traffic-oriented model to classify congestion from large dataset of ultra-low frame rate video captured from traffic surveillance system. The proposed deeply supervised traffic congestion detector has two modules: attention proposal module and deeply supervised inception network. Specifically, within the shallow layers, the binary edge/corner density features are used in attention proposal module to generate the rang of interest (ROI) mask automatically. This strategy keeps the training process focusing on the congestion features without disturbances. Following the attention proposal module, a very deep structure based on the inception network was used together to effectively extract rich and discriminative features then detect traffic congestion. The approach was tested on a self-established dataset based on empirical data, which contains images captured from 14470 surveillance cameras for monitoring 5,215 km of freeway in Shaanxi province, China. The experimental results show that the accuracy of the proposed method could reach 95.77% considering various disturbances, conditions and other limitations, which is improved than unsupervised networks. more...

This study employed a high-resolution camera to capture a visual record of an active Northern Black Swift (Cypseloides niger borealis) nest throughout an entire breeding season to document nesting activities and behaviors which are incompletely known in this species. The camera captured footage 24 hours a day from arrival of the adults at Box Canyon Falls, Ouray, Colorado, to fledging. Nest repairs began a week after arrival and were conducted 83% of the time by the female. Egg-laying occurred three weeks after arrival. The adults nearly equally shared incubation, brooding, and chick provisioning duties; the male performed 49% of effective incubation, 47% of brooding, and 51% of provisioning while the female contributed 51%, 53%, and 49%, respectively. Adults fed the nestling intermittently both day and night until age 15 days, at which time brooding ended, the nestling spent days alone, and provisioning occurred primarily at night. The nestling started wing exercises at age 13 days and they continued in increasing frequency and duration until the day prior to fledging at 51 days of age. The nesting pair exhibited cathemeral behavior, active night and day to conduct activities necessary for survival and to successfully raise their offspring. Although only one nest was observed, the surveillance camera was an effective tool for recording otherwise difficult-to-observe activities, especially those occurring at night, and revealed many previously unknown breeding activities and behaviors for this species. more...