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Journal of Electronic Imaging

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset
Author(s): Qiaoyuan Liu; Yuru Wang; Minghao Yin; Jinchang Ren; Ruizhi Li
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Paper Abstract

Although various visual tracking algorithms have been proposed in the last 2–3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the “curse of dimensionality” has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

Paper Details

Date Published: 12 December 2017
PDF: 10 pages
J. Electron. Imag. 26(6) 063025 doi: 10.1117/1.JEI.26.6.063025
Published in: Journal of Electronic Imaging Volume 26, Issue 6
Show Author Affiliations
Qiaoyuan Liu, Northeast Normal Univ. (China)
Yuru Wang, Northeast Normal Univ. (China)
Minghao Yin, Northeast Normal Univ. (China)
Jinchang Ren, Univ. of Strathclyde (United Kingdom)
Ruizhi Li, Northeast Normal Univ. (China)


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