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Lightweight Object Tracking Algorithm Based on Siamese Network with Efficient Attention

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

Siamese networks have drawn great attention in visual tracking in recently years because they have a good balance between accuracy and speed. However, in most Siamese trackers, their backbone networks used as feature extractor are relatively shallow and narrow like AlexNet, which does not take full advantage of deep neural networks. In this paper, we propose a lightweight Siamese network object tracking algorithm based on efficient attention mechanism to enhance tracking robustness and accuracy. Firstly, we modify MobileNetV2 and use it as our backbone network, it can reduces the parameters and calculation amount drastically and upgrades the speed of training and testing. Secondly, attention mechanism weighted the feature maps in channels and spatial use for distributing the contribution of the different response maps. Thirdly, different level features are fused for the purpose of obtaining more robust results. The experiments show that our tracker can improve both the accuracy and speed on three benchmarks, including OTB2015, VOT2018 and TrackingNet.

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Author information

Authors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Hanhua Yu & Qingling Liu

Authors
  1. Hanhua Yu
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  2. Qingling Liu
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Corresponding author

Correspondence to Qingling Liu .

Editor information

Editors and Affiliations

  1. Peking University, Bei**g, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Yu, H., Liu, Q. (2021). Lightweight Object Tracking Algorithm Based on Siamese Network with Efficient Attention. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_41

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

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