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Adaptive Feature Selection Siamese Networks for Visual Tracking

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Frontiers of Computer Vision (IW-FCV 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1212))

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Abstract

Recently, template based discriminative trackers, especially Siamese network based trackers have shown great potential in terms of balanced accuracy and tracking speed. However, it is still difficult for Siamese models to adapt the target variations from offline learning. In this paper, we introduced an Adaptive Feature Selection Siamese (AFS-Siam) network to learn the most discriminative feature information for better tracking. Features from different layers contain complementary information for discrimination. Proposed adaptive feature selection module selects the most useful feature information from different convolutional layers while suppresses the irrelevant ones. Proposed tracking algorithm not only alleviates the over-fitting problem but also increases the discriminative ability. The proposed tracking framework is trained end-to-end. And extensive experimental results over OTB50, OTB100, TC-128, and VOT2017 demonstrate that our tracking algorithm exhibits favorable performance compared to other state-of-the-art methods.

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Acknowledgment

This research was supported by Development project of leading technology for future vehicle of the business of Daegu metropolitan city (No. 20171105).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea

    Mustansar Fiaz, Md. Maklachur Rahman, Sehar Shahzad Farooq, Ki Yeol Baek & Soon Ki Jung

  2. Department of Computer Science, Information Technology University, Lahore, Pakistan

    Arif Mahmood

Authors
  1. Mustansar Fiaz
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  2. Md. Maklachur Rahman
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  3. Arif Mahmood
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  4. Sehar Shahzad Farooq
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  5. Ki Yeol Baek
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  6. Soon Ki Jung
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Corresponding author

Correspondence to Soon Ki Jung .

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Editors and Affiliations

  1. Saitama Institute of Technology, Saitama, Japan

    Wataru Ohyama

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Soon Ki Jung

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Fiaz, M., Rahman, M.M., Mahmood, A., Farooq, S.S., Baek, K.Y., Jung, S.K. (2020). Adaptive Feature Selection Siamese Networks for Visual Tracking. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_13

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  • DOI: https://doi.org/10.1007/978-981-15-4818-5_13

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