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Related Experiment Video

Updated: Jun 28, 2025

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Object Detection and Tracking with YOLO and the Sliding Innovation Filter.

Alexander Moksyakov1, Yuandi Wu2, Stephen Andrew Gadsden2

  • 1College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new object detection and tracking method using You Only Look Once (YOLO) and a sliding innovation filter. This approach enhances tracking reliability in dynamic environments with disturbances.

Keywords:
Kalman filterYOLOestimation theorymachine visionobject detectionsliding innovation filtertarget tracking

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Object detection and tracking are crucial in machine learning and computer vision.
  • Existing methods struggle with real-world challenges like occlusions and disturbances.

Purpose of the Study:

  • To propose a novel object detection and tracking approach.
  • To address challenges in dynamic and uncertain environments.

Main Methods:

  • Integration of You Only Look Once (YOLO) for object detection.
  • Implementation of a sliding innovation filter for robust target tracking.
  • Estimation of optimal centroid location and trajectory updating.

Main Results:

  • The proposed sliding innovation filter-based tracking outperforms traditional Kalman-based methods.
  • Enhanced tracking reliability in the presence of disturbances was demonstrated.
  • Experimental simulations in surveillance scenarios validated the approach.

Conclusions:

  • The research offers a practical and effective solution for object detection and tracking.
  • The sliding innovation filter proves robust in disturbed and uncertain environments.
  • This work provides a foundation for advancing multi-object tracking applications.