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Related Experiment Video

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association.

Changcheng Xiao1, Zhigang Luo1

  • 1School of Computer Science, National University of Defense Technology, Changsha 410000, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary

This study introduces a hierarchical association strategy for multi-pedestrian tracking (MPT) to improve performance in crowded scenes. The method effectively identifies occluded and small pedestrians missed by standard tracking-by-regression approaches.

Keywords:
MOT challengehierarchical associationmulti-pedestrian trackingspatial–temporal information

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Multi-pedestrian tracking (MPT) methods have advanced, but struggle in crowded scenes.
  • Current tracking-by-regression paradigms can miss small or occluded pedestrians.

Purpose of the Study:

  • To enhance multi-pedestrian tracking performance in crowded environments.
  • To address limitations of existing tracking-by-regression methods.

Main Methods:

  • A hierarchical association strategy is proposed for improved data association.
  • A history-aware mask is employed to identify previously missed pedestrians.
  • The approach is integrated into an end-to-end learning framework.

Main Results:

  • The hierarchical strategy significantly improves tracking in crowded scenes.
  • The method effectively detects occluded and small targets.
  • Experiments on public benchmarks validate the strategy's effectiveness.

Conclusions:

  • The proposed hierarchical association strategy enhances multi-pedestrian tracking in challenging, crowded scenarios.
  • This method offers a robust solution for identifying pedestrians missed by conventional approaches.