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An anti-occlusion optimization algorithm for multiple pedestrian tracking

Author

Listed:
  • Lijuan Zhang
  • Gongcheng Ding
  • Guanhang Li
  • Yutong Jiang
  • Zhiyi Li
  • Dongming Li

Abstract

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.

Suggested Citation

  • Lijuan Zhang & Gongcheng Ding & Guanhang Li & Yutong Jiang & Zhiyi Li & Dongming Li, 2024. "An anti-occlusion optimization algorithm for multiple pedestrian tracking," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0291538
    DOI: 10.1371/journal.pone.0291538
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