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High-Speed Tracking with Mutual Assistance of Feature Filters and Detectors.

Akira Matsuo1, Yuji Yamakawa2

  • 1Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo 153-8505, Japan.

Sensors (Basel, Switzerland)
|August 26, 2023
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Summary

The MAFiD method enhances object tracking by combining correlation filters, deep learning, and background subtraction. This novel approach achieves high-speed tracking (618 FPS) and accuracy (86% IoU) for real-time robotics applications.

Keywords:
high-speed visionimage processingmachine learningobject tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Object detection and tracking are crucial for computer vision and robotics.
  • Current methods struggle to balance high tracking speed with detection accuracy.
  • High-speed cameras require efficient real-time object tracking for robotic control.

Purpose of the Study:

  • To develop a novel object tracking method that achieves both high speed and high accuracy.
  • To improve the performance of object tracking systems for real-time applications.
  • To address the limitations of existing methods in simultaneous speed and accuracy.

Main Methods:

  • Proposed the Mutual Assist tracker of feature Filters and Detectors (MAFiD) method.
  • Combined correlation filter-based tracking, deep learning-based detection, and background subtraction.
  • Algorithms operate in parallel, mutually assisting each other for enhanced performance.

Main Results:

  • Achieved a tracking speed of 618 frames per second (FPS).
  • Reached an accuracy of 86% Intersection over Union (IoU).
  • Demonstrated a detection latency of 3.48 ms.

Conclusions:

  • The MAFiD method successfully achieves high-speed object tracking with high detection accuracy.
  • Experimental results surpass conventional methods, validating the MAFiD approach.
  • This advancement contributes significantly to object-tracking technology for robotics and computer vision.