Incremental learning-based visual tracking with weighted discriminative dictionaries

Penggen Zheng, Huimin Zhao, Jin Zhan (Lead / Corresponding author), Yijun Yan, Jinchang Ren, Jujian Lv, Zhihui Huang

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Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalInternational Journal of Advanced Robotic Systems
Issue number6
Early online date4 Dec 2019
Publication statusPublished - Dec 2019


  • incremental update
  • similarity weights
  • sparse representation
  • Visual tracking
  • weighted dictionary

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence


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