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Training-Based Methods for Comparison of Object Detection Methods for Visual Object Tracking.

Ahmad Delforouzi1, Bhargav Pamarthi2, Marcin Grzegorzek3

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Summary
This summary is machine-generated.

This study compares deep learning object detectors for video tracking. The Adaptive Component Features (ACF) and You Only Look Once (YOLO) trackers demonstrated superior stability in challenging video sequences.

Keywords:
Kalman filterdeep learningobject detectionobject trackingonline training

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Object tracking in videos is a significant challenge in machine vision.
  • Deep learning object detectors excel in still images but face a semantic gap in video tracking applications.

Purpose of the Study:

  • To comparatively evaluate prominent learning-based object detectors for video object tracking.
  • To investigate the effectiveness of online and offline training methods for object tracking.

Main Methods:

  • Comparative analysis of object detectors: ACF, R-CNN, Fast R-CNN, Faster R-CNN, and YOLO.
  • Implementation of online tracking (detector updated with synthetic and detected frames) and offline tracking (detector on still images, Kalman filter for association).
  • Evaluation on the TLD dataset, known for challenging tracking scenarios.

Main Results:

  • Adaptive Component Features (ACF) and You Only Look Once (YOLO) trackers exhibited greater stability compared to other evaluated methods.
  • The study provides open-source code for reproducibility and further research.

Conclusions:

  • ACF and YOLO show promise for robust object tracking in challenging video conditions.
  • The comparative study offers valuable insights for selecting appropriate detectors for video tracking tasks.