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27 April 2018 A hybrid local and global multi-object tracking with semantic spatial and appearance modules
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Multi-object tracking is one of the most challenging problem among computer vision applications due to computational cost, partial or full occlusions, crowded scenes, and etc. It has many real-life applicable uses from surveillance to video analysis and video summarization. In this paper, We propose a hybrid tracking-by-detection system that combines local and global data association scheme to ensure efficiency and reduce complexity. In local data association, spacial and appearance modules are used to ensure first step assignment for the strongest object matching. Then tracklet linking is applied during global data association step after filtering out all unreliable and distractor hypotheses using spacial, temporal and appearance descriptors. Our framework can handle the appearance of new objects, temporal disappearance, object terminations, and object occlusions. Our experiments on MOT16 dataset 1 that consisting of challenging real-world videos shows the integration between local and global data association is important and having promising performance.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Noor M. Al-Shakarji, Filiz Bunyak, Guna Seetharaman, and Kannappan Palaniappan "A hybrid local and global multi-object tracking with semantic spatial and appearance modules", Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450B (27 April 2018);

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