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Hierarchical convolutional features for end-to-end representation-based visual tracking

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Abstract

Recently, deep learning is widely developed in computer vision applications. In this paper, a novel simple tracker with deep learning is proposed to complete the tracking task. A simple fully convolutional Siamese network is applied to capture the similarity between different frames. Nevertheless, the detailed information from lower layers, which is also important for locating the target object, is not considered into the tracking task. In this paper, the detailed information from two lower layers is considered into the response map to improve the performance and not to increase much time spent. This leads more significant improvement for feature representation and localization of the target object. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the baseline and the state-of-the-art trackers.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61472110, 61772161,61532006, 61320106006 and 61601158, by the Zhejiang Provincial Science Foundation under Grants LQ16F030004.

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Authors and Affiliations

  1. Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Suguo Zhu, Zhenying Fang & Fei Gao

  2. State Key Laboratory of Integrated Services Networks, Xidian University, Xian, 710071, China

    Fei Gao

Authors
  1. Suguo Zhu
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  2. Zhenying Fang
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  3. Fei Gao
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Correspondence to Suguo Zhu.

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Zhu, S., Fang, Z. & Gao, F. Hierarchical convolutional features for end-to-end representation-based visual tracking. Machine Vision and Applications 29, 955–963 (2018). https://doi.org/10.1007/s00138-018-0947-6

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  • DOI: https://doi.org/10.1007/s00138-018-0947-6

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