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Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System.

Chunmei Liu1, Yirui Wang2, Shangce Gao3

  • 1Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive kernel for mean shift tracking, improving robot vision by accurately tracking object shape and position. The method enhances contour detection and avoids background clutter for robust performance.

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Object tracking in robot vision systems is challenged by variations in object shape and background clutter.
  • Existing mean shift trackers often struggle with adaptive kernel shape construction for accurate contour tracking.

Purpose of the Study:

  • To develop an adaptive shape kernel for mean shift tracking using a single static camera.
  • To address the challenge of constructing a kernel shape that dynamically adapts to object shape variations.

Main Methods:

  • Nonlinear manifold learning is employed to create a low-dimensional shape space from training data.
  • The proposed kernel searches this shape space to construct an adaptive kernel in the high-dimensional space.
  • The adaptive kernel enhances the mean shift tracker's ability to follow object position and contour.

Main Results:

  • The adaptive kernel significantly improves the accuracy of tracking both object position and contour.
  • The method demonstrates robustness in avoiding background clutter, leading to more reliable tracking.
  • Experimental validation using walking humans confirms the accuracy and robustness of the proposed tracking method.

Conclusions:

  • The proposed adaptive shape kernel-based mean shift tracker offers a significant advancement for robot vision systems.
  • This approach effectively handles object shape adaptability and background clutter, enhancing tracking precision.
  • The method provides accurate human position and contour description, validating its practical utility.