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Image-based Lagrangian Particle Tracking in Bed-load Experiments
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Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

Siqiu Guo1,2, Tao Zhang3, Yulong Song4

  • 1Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, 3888 Dongnanhu Road, Changchun 130033, China. guo_qiuqiu@163.com.

Sensors (Basel, Switzerland)
|April 26, 2018
PubMed
Summary

This study introduces an improved particle swarm tracking algorithm using color features and a novel inertia weight adjustment mechanism. The enhanced method offers robust object tracking, even with occlusion and deformation, achieving state-of-the-art performance.

Keywords:
color featureinertia weightobject trackingparticle maturityparticle swarm optimization

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Object tracking is crucial in computer vision.
  • Traditional particle swarm optimization (PSO) algorithms face limitations in object tracking, particularly with occlusion and non-rigid deformations.
  • Existing inertia weight adjustment mechanisms in PSO lack adaptability to particle states.

Purpose of the Study:

  • To develop an improved particle swarm tracking algorithm robust to deformations, scale variations, rotations, and partial occlusions.
  • To enhance the inertia weight adjustment mechanism in PSO for adaptive tracking.
  • To achieve state-of-the-art performance in object tracking scenarios.

Main Methods:

  • Utilizing a weighted color histogram as the target feature to mitigate the impact of edge pixels.
  • Implementing an improved inertia weight adjustment mechanism based on the concept of 'particle maturity'.
  • Applying particle swarm optimization (PSO) for multi-peak search to handle target occlusion.

Main Results:

  • The proposed algorithm demonstrates insensitivity to non-rigid deformation, scale variation, and rotation.
  • Reduced influence of partial obstruction on target feature description.
  • Experimental results indicate state-of-the-art performance across diverse tracking scenarios.

Conclusions:

  • The enhanced particle swarm tracking algorithm provides accurate and robust object tracking.
  • The 'particle maturity' concept effectively improves inertia weight adaptation in PSO for tracking.
  • The algorithm shows significant improvements in handling challenging tracking conditions.