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Exploiting Temporal Coherence for Self-Supervised One-Shot Video Re-identification

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12372))

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Abstract

While supervised techniques in re-identification are extremely effective, the need for large amounts of annotations makes them impractical for large camera networks. One-shot re-identification, which uses a singular labeled tracklet for each identity along with a pool of unlabeled tracklets, is a potential candidate towards reducing this labeling effort. Current one-shot re-identification methods function by modeling the inter-relationships amongst the labeled and the unlabeled data, but fail to fully exploit such relationships that exist within the pool of unlabeled data itself. In this paper, we propose a new framework named Temporal Consistency Progressive Learning, which uses temporal coherence as a novel self-supervised auxiliary task in the one-shot learning paradigm to capture such relationships amongst the unlabeled tracklets. Optimizing two new losses, which enforce consistency on a local and global scale, our framework can learn richer and more discriminative representations. Extensive experiments on two challenging video re-identification datasets - MARS and DukeMTMC-VideoReID - demonstrate that our proposed method is able to estimate the true labels of the unlabeled data more accurately by up to \(8\%\), and obtain significantly better re-identification performance compared to the existing state-of-the-art techniques .

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Acknowledgments

We thank Sourya Roy, Sujoy Paul and Abhishek Aich for their assistance, advice and critique. The work was partially supported by NSF grant 1544969 and ONR grant N00014-19-1-2264.

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Authors and Affiliations

  1. University of California, Riverside, USA

    Dripta S. Raychaudhuri & Amit K. Roy-Chowdhury

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  1. Dripta S. Raychaudhuri
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  2. Amit K. Roy-Chowdhury
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Correspondence to Dripta S. Raychaudhuri .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Raychaudhuri, D.S., Roy-Chowdhury, A.K. (2020). Exploiting Temporal Coherence for Self-Supervised One-Shot Video Re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-58583-9_16

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