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Scale estimation-based visual tracking with optimized convolutional activation features

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Abstract

Convolutional neural networks (CNNs) have proven very effective for learning features in visual tracking. While working effectively, it is still very challenging due to the scale variations and deformation, which may cause inconsecutive tracking trajectory and distraction. In this paper, pre-train deep learning network architecture is adopted for visual tracking, by introducing a spectral pooling in the network. Then, we propose an algorithm which, by interpreting scale correlation filters as the corresponding function of convolution filters in deep neural networks, exploits multilevel CNNs activation features into a new tracking framework. Finally, two-stage fine-tuning is then introduced for updating the model to keep long-time tracking. We test the proposed tracking method on large-scale benchmark sequences. Experimental results illustrate the effectiveness of the proposed algorithm compared with other state-of-the-art methods.

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Acknowledgements

This work is supported by the National Natural Sciences Foundation of China under Grant Nos. 61603415, 61602322, 61503274 and the Fundamental Research Funds for the Central Universities under Grant No. D2019021.

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Authors and Affiliations

  1. Criminal Investigation Department, Criminal Investigation Police University of China, Shenyang, 110854, China

    Qiang Guo, Xuefei Cao & Qinglong Zou

  2. Police Terrorism Research Center, Criminal Investigation Police University of China, Shenyang, 110854, China

    Qiang Guo, Xuefei Cao & Qinglong Zou

Authors
  1. Qiang Guo
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  2. Xuefei Cao
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  3. Qinglong Zou
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Correspondence to Qiang Guo.

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Guo, Q., Cao, X. & Zou, Q. Scale estimation-based visual tracking with optimized convolutional activation features. Machine Vision and Applications 30, 1263–1273 (2019). https://doi.org/10.1007/s00138-019-01049-1

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