Collaborative model with adaptive selection scheme for visual tracking

Abstract

Visual tracking is a challenging task since it involves developing an effective appearance model to deal with numerous factors. In this paper, we propose a robust object tracking algorithm based on a collaborative model with adaptive selection scheme. Specifically, based on the discriminative features selected from the feature selection scheme, we develop a sparse discriminative model (SDM) by introducing a confidence measure strategy. In addition, we present a sparse generative model (SGM) by combining ℓ1 regularization with PCA reconstruction. In contrast to existing hybrid generative discriminative tracking algorithms, we propose a novel adaptive selection scheme based on the Euclidean distance as the joint mechanism, which helps to construct a more reasonable likelihood function for our collaborative model. Experimental results on several challenging image sequences demonstrate that the proposed tracking algorithm leads to a more favorable performance compared with the state-of-the-art methods.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (61362030, 61201429), the Project Funded by China Postdoctoral Science Foundation (2015M581720, 2016M600360), the Project Funded by Jiangsu Postdoctoral Science Foundation (1601216C) and Technology Research Project of the Ministry of Public Security of China (2014JSYJB007).

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Correspondence to Jun Kong.

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Liu, T., Kong, J., Jiang, M. et al. Collaborative model with adaptive selection scheme for visual tracking. Int. J. Mach. Learn. & Cyber. 10, 215–228 (2019). https://doi.org/10.1007/s13042-017-0709-1

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Keywords

  • Visual tracking
  • Collaborative model
  • Adaptive selection scheme
  • Sparse representation