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Robust and Real-Time Visual Tracking Based on Single-Layer Convolutional Features and Accurate Scale Estimation

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Image and Graphics Technologies and Applications (IGTA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 875))

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Abstract

Visual tracking is a fundamental problem in computer vision. Recently, some methods have been developed to utilize features learned from a deep convolutional neural network for visual tracking and achieve record-breaking performances. However, deep trackers suffer from efficiency. In this paper, we propose an object tracking method combining the single-layer convolutional features with correlation filter to locate and speed up. Meanwhile accurate scale prediction and high-confidence model update strategy are adopted to solve the scale variation and similarity interfere problems. Extensive experiments on large scale benchmarks demonstrate the effectiveness of the proposed algorithm against state-of-the-art trackers.

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Acknowledgments

This work is supported in part by National Key R&D Program of China, 2017YFC0821102, in part by North China University of Technology Students’ Technological Activity.

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Authors and Affiliations

  1. School of Sciences, North China University of Technology, Beijing, 100144, China

    Runling Wang & Jiancheng Zou

  2. Beijing Key Laboratory of Urban Intelligent Control Technology, North China University of Technology, Beijing, 100144, China

    Manqiang Che & Changzhen Xiong

Authors
  1. Runling Wang
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  2. Jiancheng Zou
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  3. Manqiang Che
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  4. Changzhen Xiong
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Corresponding author

Correspondence to Runling Wang .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Beihang University, Beijing, China

    Zhiguo Jiang

  3. Peking University, Beijing, China

    Yuxin Peng

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Wang, R., Zou, J., Che, M., Xiong, C. (2018). Robust and Real-Time Visual Tracking Based on Single-Layer Convolutional Features and Accurate Scale Estimation. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_47

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  • DOI: https://doi.org/10.1007/978-981-13-1702-6_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1701-9

  • Online ISBN: 978-981-13-1702-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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