Abstract
Multi-target tracking (MTT) has been a research hotspot in the field of computer vision. The objective is forming the trajectory of multiple targets in a given video. However, the useful detection and tracklet relationship during the tracking process are not fully explored in most current algorithms and it leads to the accumulation of errors. We introduce a novel Detection Group, which includes the detections within a temporal and spatial threshold and then model the relationship between Detection Group(DG) and close tracklets. Although the minimum-cost network flow algorithm has been proven to be a successful strategy for multi-target tracking, but it still has one main drawback: due to the fact that useful corresponding detection and tracklet relationships are not well modeled, the network flow based tracker can only model low-level detection relationship without high-level detection set information. To cope with this problem, we extend the classical minimum-cost network flow algorithm within the tracking-by-detection paradigm by incorporating additional constraints. In our experiment, we achieved encouraging result on the MOT17 benchmark and our result is comparable to the current state of the art trackers.
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Acknowledgement
This study is partially supported by the National Key R&D Program of China (No. 2017YFC0803700), the National Natural Science Foundation of China (No. 61 472019), the Macao Science and Technology Development Fund (No. 138/2016/A3), the Program of Introducing Talents of Discipline to Universities and the Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09, the Project of Experimental Verification of the Basic Commonness and Key Technical Standards of the Industrial Internet network architecture. Thank you for the support from HAWKEYE Group.
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Li, C., Qian, K., Chen, J., Xue, G., Sheng, H., Ke, W. (2018). Enhancing Network Flow for Multi-target Tracking with Detection Group Analysis. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_15
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DOI: https://doi.org/10.1007/978-3-319-99365-2_15
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