Multiple feature fusion via covariance matrix for visual tracking

Zefenfen Jin; Zhiqiang Hou; Wangsheng Yu; Xin Wang; Hui Sun

doi:10.1117/12.2303660

10 April 2018 Multiple feature fusion via covariance matrix for visual tracking

Zefenfen Jin, Zhiqiang Hou, Wangsheng Yu, Xin Wang, Hui Sun

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106153S (2018) https://doi.org/10.1117/12.2303660
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

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Zefenfen Jin, Zhiqiang Hou, Wangsheng Yu, Xin Wang, and Hui Sun "Multiple feature fusion via covariance matrix for visual tracking ", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153S (10 April 2018); https://doi.org/10.1117/12.2303660

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KEYWORDS

Detection and tracking algorithms

Optical tracking

Quantum efficiency

Video

Genetic algorithms

Error analysis

Optimization (mathematics)

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