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Player Tracking Evaluation with YOLOv8, ByteTrack, and BoT-SORT

Overview

This project evaluates the performance of different tracking algorithms for player tracking in volleyball match videos. The focus is on comparing the YOLOv8 Default Tracker, ByteTrack, and BoT-SORT in terms of tracking accuracy, identity consistency, and robustness.

Trackers Evaluated

  • YOLOv8 Default Tracker: Baseline tracker using YOLOv8’s default tracking mechanism.
  • ByteTrack: Combines strong detection with a simple association mechanism to improve tracking accuracy.
  • BoT-SORT: Enhances tracking with appearance features and advanced motion models for improved identity preservation.

Procedure

1. Dataset Preparation

  • Volleyball match videos featuring multiple players.
  • Ensured videos captured complex player interactions to test tracker robustness.

2. Detection and Tracking

  • YOLOv8 was used for object detection across all trackers.
  • Each tracker (YOLOv8 Default, ByteTrack, BoT-SORT) was applied to the videos.
  • Player bounding boxes and identity assignments were generated for each frame.

3. Qualitative Analysis

  • BoT-SORT Tracker:
    • Maintained consistent IDs and bounding boxes.
  • YOLOv8 Tracker:
    • Efficient but exhibited lower confidence scores.
  • ByteTrack:
    • Accurate bounding boxes and high confidence scores.
  • BoT-SORT:
    • Best at maintaining player identities due to appearance features and advanced motion models.

4. Challenges Noted

  • Identity Switches:
    • Reduced in BoT-SORT but still present during complex interactions.
  • Computational Load:
    • Advanced trackers like BoT-SORT require more resources, impacting real-time feasibility.
  • False Positives:
    • Low-confidence detections led to occasional tracking of non-player objects.

Results Summary

  • BoT-SORT: Best performance in maintaining player identities.
  • ByteTrack: Accurate bounding boxes with fewer identity switches.
  • YOLOv8 Default Tracker: Efficient but less accurate in maintaining consistent IDs.

Future Improvements

  • Optimize BoT-SORT for real-time performance.
  • Reduce false positives by refining confidence thresholds.
  • Further minimize identity switches during complex interactions.

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