| Citation: | WANG Xin, HOU Zhiqiang, YU Wangsheng, et al. Robust visual tracking based on deep sparse learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788(in Chinese)
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In visual tracking, the efficient and robust feature representation plays an important role in tracking performance in complicated environment. Therefore, a deep sparse neural network model which can extract more intrinsic and abstract features was proposed. Meanwhile, the complex and time-consuming pre-training process was avoided by using this model. During online tracking, the method of data augmentation was employed in the single positive sample to balance the quantities of positive and negative samples, which can improve the stability of the model. The local confidence maps were generated through dense sampling search to overcome the phenomenon of sampling particle drift. In order to improve the robustness of the model, several corresponding strategies of updating model parameters and searching area are proposed respectively. Extensive experimental results indicate the effectiveness and robustness of the proposed algorithm in challenging environment compared with state-of-the-art tracking algorithms. The problem of tracking drift is alleviated significantly and the tracking speed is fast.
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