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DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

Visual object tracking is a fundamental task in the field of computer vision. Recently, Siamese trackers have achieved state-of-the-art performance on recent benchmarks. However, Siamese trackers do not fully utilize semantic and objectness information from pre-trained networks that have been trained on image classification task. Furthermore, the pre-trained Siamese architecture is sparsely activated by the category label, which leads to unnecessary calculations and overfitting. In this paper, we propose to learn a Domain-Aware that fully utilizes semantic and objectness information while producing a class-agnostic using a ridge regression network. Moreover, to reduce the sparsity problem, we solve the ridge regression problem with a differentiable weighted-dynamic loss function. Our tracker, dubbed DomainSiam, improves the feature learning in the training phase and generalization capability to other domains. Extensive experiments are performed on five tracking benchmarks, including OTB2013 and OTB2015, for a validation set as well as VOT2017, VOT2018, LaSOT, TrackingNet, and GOT10k for a testing set. DomainSiam achieves a state-of-the-art performance on these benchmarks while running at 53 FPS.

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Notes

  1. 1.

    https://vip-mun.github.io/DomainSiam.

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Author information

Authors and Affiliations

  1. Computer Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Mohamed H. Abdelpakey & Mohamed S. Shehata

  2. Department of Computer Science, Math, Physics, and Statistics, University of British Columbia, Kelowna, BC, Canada

    Mohamed S. Shehata

Authors
  1. Mohamed H. Abdelpakey
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  2. Mohamed S. Shehata
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Corresponding author

Correspondence to Mohamed H. Abdelpakey .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Abdelpakey, M.H., Shehata, M.S. (2019). DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_4

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  • Online ISBN: 978-3-030-33720-9

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