Abstract
Occlusion is one of the main challenges in multi-target tracking, which causes fragments in tracking. In order to handle with fragments, various motion models were proposed. However, motion model has limited effect on dealing with long-term fragments, because the predictability of target motion declines with increase in fragment length. Thus we propose a robust local effective matching model for partial detections to reduce fragment length first. The proposed model is integrated into a network flow based hierarchical framework to solve long-term fragments step-by-step. Initial tracklets are generated for later analysis in the first level. The robust local effective matching model is used in the second level to reduce fragment length. A motion model is utilized in the third level to solve fragments between tracklets. The benchmark results on 2D MOT 2015 dataset were compared with several state-of-the-art trackers and our method got competitive results with those trackers.
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Acknowledgment
This study is partially supported by the National Natural Science Foundation of China (No. 61472019), the National Science Technology Pillar Program (No. 2015BAF14B01), the Macao Science and Technology Development Fund (No. 138/2016/A3), the Programme of Introducing Talents of Discipline to Universities, the Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09 and HAWKEYE Group.
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Sheng, H., Hao, L., Chen, J., Zhang, Y., Ke, W. (2018). Robust Local Effective Matching Model for Multi-target Tracking. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_23
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DOI: https://doi.org/10.1007/978-3-319-77383-4_23
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