Abstract
We present a novel global graph matching framework based on virtual nodes for multi-object tracking in multiple views. Contrary to recent approaches, we incorporate a global graph matching structure (GGMS), allowing the tracker to better cope with long-term occlusions and tracking failure caused by interaction of targets. In our approach, the matching problem is solved as follows: Virtual detections are introduced by mapping the nodes among views, to ensure that the amount of detections in each view is the same, and then realize the whole graph matching. In addition, appropriate optimization is performed to convert this mapping problem to the Assignment Problem, which could be efficiently addressed by the Hungarian Algorithm. Finally, we demonstrate the validity of our approach on the publicly available datasets, and achieve very competitive results by quantitative evaluation.
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Acknowledgement
This study was partially supported by the National Natural Science Foundation of China (No. 61370122) and the National High Technology Research and Development Program of China (No. 2013AA01A603). Supported by the Programme of Introducing Talents of Discipline to Universities and the Open Fund of the State Key Laboratory of Software Development Environment under grant #SKLSDE-2015ZX-21. Thank you for the support from HAWKEYE Group.
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Li, C., Ping, S., Sheng, H., Chen, J., Xiong, Z. (2016). Multi-view Multi-object Tracking Based on Global Graph Matching Structure. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_65
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DOI: https://doi.org/10.1007/978-3-319-48896-7_65
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