Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
122 Downloads (Pure)


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 et al. 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 caused low efficiency and ambiguity caused poor performance. In this paper, 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, “Curse of Dimensionality” has been avoided whilst 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.
Original languageEnglish
Article number063025
Number of pages10
JournalJournal of Electronic Imaging
Issue number6
Early online date12 Dec 2017
Publication statusE-pub ahead of print - 12 Dec 2017


  • visual tracking
  • feature subset
  • decontaminate
  • evolutionary algorithm

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