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Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies.

Longteng Kong1, Mengxiao Zhu2, Nan Ran1

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Summary
This summary is machine-generated.

This study introduces a new method for tracking multiple athletes in sports videos by analyzing long-term pose dynamics. The approach effectively distinguishes athletes despite appearance similarities and occlusions, improving sports analytics.

Keywords:
Multi-Athlete Tracking (MAT)long short-term memory (LSTM) networkssports video analysis

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Sports Analytics

Background:

  • Multi-athlete tracking (MAT) in sports video analysis faces challenges due to similar appearances and frequent occlusions.
  • Existing tracking methods are often insufficient for complex MAT scenarios.

Purpose of the Study:

  • To develop a novel online multiple athlete tracking approach.
  • To enhance athlete distinction using long-term temporal pose dynamics.

Main Methods:

  • Designed a Pose-based Triple Stream Network (PTSN) utilizing Long Short-Term Memory (LSTM) for modeling pose dynamics (appearance, motion, interaction).
  • Developed a multi-state online matching algorithm employing bipartite graph matching and PTSN-generated similarity scores.
  • The algorithm incorporates multiple detection states for robustness against noisy detections and occlusions.

Main Results:

  • The proposed PTSN effectively models long-term temporal pose dynamics for athlete distinction.
  • The multi-state online matching algorithm demonstrates robustness in challenging tracking conditions.
  • Evaluated on APIDIS, NCAA Basketball, and VolleyTrack datasets, confirming the method's effectiveness.

Conclusions:

  • The novel online MAT approach effectively addresses challenges of athlete similarity and occlusion.
  • Leveraging long-term temporal pose dynamics significantly improves tracking accuracy in sports videos.
  • The method offers a robust solution for sports video analysis applications.