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Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator.

Hosik Choi1, Byungmun Kang1, DaeEun Kim1

  • 1School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

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This study presents a novel sparse optical flow algorithm for efficient moving object detection and tracking. The method enhances accuracy and speed, even with occlusions and varying conditions.

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Conventional moving object detection methods suffer from high computational costs, noise sensitivity, and target disappearance due to occlusions.
  • Existing techniques struggle with real-world complexities like varied illumination and dynamic environments.

Purpose of the Study:

  • To introduce a new moving object detection and tracking algorithm utilizing sparse optical flow.
  • To address limitations of conventional methods by reducing computation time, enhancing noise reduction, and improving target estimation efficiency.

Main Methods:

  • Developed a sparse optical flow-based algorithm incorporating a moving window detector for feature point selection based on location history.
  • Implemented a memory-based estimator to retain corner feature locations, enabling tracking of obscured targets.
Keywords:
moving object trackingmoving windowoptical flowtarget estimator

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  • Evaluated the algorithm on an embedded platform (Raspberry Pi 4) under diverse real-world conditions.

    • Main Results:

    • The proposed algorithm significantly improves moving object detection performance.
    • Achieved high frames per second (FPS) and enhanced accuracy compared to traditional optical flow, Haar-like, and HOG methods.
    • Demonstrated effective tracking of targets even when obscured by obstacles.

    Conclusions:

    • The novel sparse optical flow algorithm offers a robust and efficient solution for moving object detection and tracking.
    • The method shows superior performance in challenging environments with varying illumination, multiple objects, and occlusions.
    • The developed approach is suitable for real-time applications on embedded systems.