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Multi-object tracking (MOT), which is crucial for computer vision and video processing, has immense potential for improvement. Traditional tracking-by-detection approaches include feature-based object re-identification methods that use trained features, but these methods suffer from a lack of suitable training data. In training datasets used for MOT, every object in a video sequence must have its own location and ID. However, assigning IDs to each object in every sequence is considerably labor-intensive, and hence current MOT datasets are unsuitable for training re-identification networks. To resolve this issue, this paper proposes a novel self-supervised learning method using several short videos that contain no human-added labels, based on the idea that each video is a set of temporally corresponding image frames. We then describe how to improve tracking performance using a re-identification network trained in a self-supervised manner. In addition, ablation studies were conducted in order to define the optimal parameters, such as number of clips, data augmentation, and appropriate matching algorithms. The proposed approach achieved competitive performance compared with current best-practice methods including supervised methods, achieving MOT accuracy = 62.0% and ID F1-score = 62.7% on the MOT17 benchmark. [ABSTRACT FROM AUTHOR]

Depth completion is the task of reconstructing dense depth images from sparse LiDAR data. LiDAR depth completion, for which LiDAR data is the only input, is an ill-posed and challenging problem owing to the underlying properties of LiDAR data: extremely few points, presence of discontinuities, and absence of texture information. Accordingly, most approaches are heavily dependent on guided color images, which leads to unsatisfactory results when the color images are degraded. To alleviate the dependency on color images but leverage this information during training, we present a deep convolutional neural network (CNN) consisting of depth and edge CNNs via transferring of knowledge. In order to compensate for the limitations of LiDAR data, we design the edge CNN to learn a gradient depth image from a powerful teacher network through the Knowledge-Distillation method. Since the teacher network is trained with color images, color-embedded information can be obtained in the test phase even if color images are not used as an input. We further propose a Self-Distillation method for transferring the color-embedded features from the edge CNN to the depth CNN. Enforcing the depth features to contain edge information hardly observed in LiDAR data enables the depth CNN to generate more edge-attentive and structure-preserving results. Our novel methods show remarkable results in outdoor and indoor environments for KITTI and NYU-Depth-V2 datasets. Experiments performed with low-channel LiDAR data in KITTI and few depth points in the NYU-Depth-V2 dataset show that our method is robust to data sparsity and applicable in various scenarios. [ABSTRACT FROM AUTHOR]

Various sensors can be attached and added to autonomous vehicles, included visual cameras, radar, LiDAR (Light Detection And Ranging), and GNSS (Global Navigation Satellite System). These sensors have been studied in many research areas, in particular studies on building precise 3D maps. It is essential for autonomous driving to create an accurate 3D map of the surrounding scene. However, creating an accurate static 3D map is difficult due to changes in moving objects or dynamic environments. Spurious objects on the 3D map can be handled by removing or ignoring them for 3D mapping. Following this idea, we propose an object segmentation and inpainting network. The proposed network called SAM-Net, addresses the object duplication issue by segmenting the objects and inpainting them with the segmentation results. Conventional inpainting research has dealt with RGB images. No matter how well such approaches reconstruct holes or corrupted images, they do not establish 3D points’ relationship with the point cloud frame. Therefore, we suggest a depth inpainting method for outdoor object segmentation and inpainting tasks that utilizes a high-precision depth range sensor (Velodyne HDL-64E), which is not suggested before. Unfortunately, no dataset exists for the outdoor depth inpainting task. Thus, to train our model, we generate a new dataset by locating objects on a clean static background. Moreover, our proposed method shows outstanding depth performance compared to the previous visual inpainting method. Our dataset will be available at: “ https://github.com/JunhyeopLee/lidar%5finpainting ”. [ABSTRACT FROM AUTHOR]

Detecting distinctive interest points in a scene or an object allows estimating which details a human finds interesting in advance to understand the scene or the object. This also forms the important basis of a variety of latter tasks related to visual detection and tracking. In this paper, we propose a simple but effective approach to extract the feature from a depth image, namely Protuberance of Depth (PoD). The proposed approach semantically explores the inherent feature representing three-dimensional protuberance by using depth which only contains two-dimensional distance information. Our approach directly allows detecting consistent interest points in a depth image. The experimental results show that our method is effective against the isometric deformation and rotation of a depth region and is applicable for real-time applications. • We propose an interest point detection method based on Protuberance of Depth (PoD). • PoD consistently extracts the feature of protuberance from a depth image. • Our method effectively detects distinctive interest points with high repeatability. • The method shows good tolerance to the rotation of a depth region. • The method is efficient enough for real-time applications. [ABSTRACT FROM AUTHOR]

Abstract: We propose a face detection method based on skin color likelihood via a boosting algorithm which emphasizes skin color information while deemphasizing non-skin color information. A stochastic model is adapted to compute the similarity between a color region and the skin color. Both Haar-like features and Local Binary Pattern (LBP) features are utilized to build a cascaded classifier. The boosted classifier is implemented based on skin color emphasis to localize the face region from a color image. Based on our experiments, the proposed method shows good tolerance to face pose variation and complex background with significant improvements over classical boosting-based classifiers in terms of total error rate performance. [Copyright &y& Elsevier]

Abstract: The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations include the use of a common covariance function among each class, and the limited dimensionality inherent to the definition of the between-class scatter. Since these problems are inherently caused by the definition of the Fisher''s criterion itself, they may not be solvable under the conventional LDA framework. This paper proposes to adopt a margin-based between-class scatter and a regularization process to resolve the issue. Essentially, we redesign the between-class scatter matrix based on the SVM margins to facilitate an effective and reliable feature extraction. This is followed by a regularization of the within-class scatter matrix. Extensive empirical experiments are performed to compare the proposed method with several other variants of the LDA method using the FERET, AR, and CMU-PIE databases. [Copyright &y& Elsevier]