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A generic MOT boosting framework by combining cues from SOT, tracklet and re-identification

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Abstract

In this paper, we propose a generic boosting framework for multiple object tracking (MOT). Unlike other works tracking objects from zero, our framework uses their results (tracklets) and makes further optimizations. The motivation of us derives from the observation that most modern MOT trackers have been acceptable performance and can yield relatively reliable tracklets; accordingly, we straight focus on the tracklet-level re-identification, which is the most challenging issue in this case. To achieve that goal, we simultaneously utilize the techniques of single object tracking, tracking fragment (tracklets) and re-identification mechanism through casting them into a multi-label energy optimization and then innovatively solving it using the \(\alpha -\)expansion with label costs algorithm. All these techniques inspire recent MOT a lot to mitigate the occlusion problem, but to our knowledge, by far few works explore to reasonably combine them all like us. Furthermore, we introduce a spatial attention to improve the appearance model and a hierarchical clustering as post-process to progressively improve the tracking consistency. Finally, testing results on the most used benchmarks demonstrate the significant effectiveness and generality of our framework, and the importance of each contribution is also verified through ablative studies.

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Notes

  1. Codes are available at: https://vision.cs.uwaterloo.ca/files/gco-v3.0.zip.

  2. https://github.com/xstgavin/amilan-motchallenge-devkit.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 61906210).

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Authors and Affiliations

  1. Science and Technology on Parallel and Distributed Processing, National University of Defense Technology, Changsha, 410073, China

    Tianyi Liang, Long Lan & Zhigang Luo

  2. Institute for Quantum Information & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China

    Long Lan & Xiang Zhang

  3. Peng cheng Laboratory, Shenzhen, China

    Long Lan

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  1. Tianyi Liang
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  2. Long Lan
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  3. Xiang Zhang
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  4. Zhigang Luo
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Correspondence to Long Lan.

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Liang, T., Lan, L., Zhang, X. et al. A generic MOT boosting framework by combining cues from SOT, tracklet and re-identification. Knowl Inf Syst 63, 2109–2127 (2021). https://doi.org/10.1007/s10115-021-01576-2

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