Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

Qiaoyuan Liu; Yuru Wang; Minghao Yin; Jinchang Ren; Ruizhi Li

doi:10.1117/1.JEI.26.6.063025

12 December 2017 Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

Qiaoyuan Liu, Yuru Wang, Minghao Yin, Jinchang Ren, Ruizhi Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063025 (December 2017). https://doi.org/10.1117/1.JEI.26.6.063025

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.

Citation Download Citation

Qiaoyuan Liu, Yuru Wang, Minghao Yin, Jinchang Ren, and Ruizhi Li "Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset," Journal of Electronic Imaging 26(6), 063025 (12 December 2017). https://doi.org/10.1117/1.JEI.26.6.063025

Received: 19 May 2017; Accepted: 26 October 2017; Published: 12 December 2017

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