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Gait Recognition from Occluded Sequences in Surveillance Sites

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Gait recognition is a challenging problem in real-world surveillance scenarios where the presence of occlusion causes only fractions of a gait cycle to get captured by the monitoring cameras. The few occlusion handling strategies in gait recognition proposed in recent years fail to perform reliably and robustly in the presence of an incomplete gait cycle. We improve the state-of-the-art by developing novel deep learning-based algorithms to identify the occluded frames in an input sequence and henceforth reconstruct these occluded frames. Specifically, we propose a two-stage pipeline consisting of occlusion detection and reconstruction frameworks, in which occlusion detection is carried out by employing a VGG-16 model, following which an LSTM-based network termed RGait-Net is employed to reconstruct the occluded frames in the sequence. The effectiveness of our method has been evaluated through the reconstruction Dice score as well as through the gait recognition accuracy obtained by computing the Gait Energy Image feature from the reconstructed sequence. Extensive evaluation using public data sets and comparative study with other methods verify the suitability of our approach for potential application in real-life scenarios.

D. Das and A. Agarwal—Contributed equally to the work.

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Acknowledgments

The authors would like to thank NVIDIA for supporting their work with a TITAN Xp GPU. The authors also acknowledge KLA Corporation for assisting them in attending the conference with an international travel grant.

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Authors and Affiliations

  1. Indian Institute of Technology (BHU), Varanasi, 221005, India

    Dhritimaan Das & Pratik Chattopadhyay

  2. Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Ayush Agarwal

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  1. Dhritimaan Das
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  2. Ayush Agarwal
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  3. Pratik Chattopadhyay
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Correspondence to Pratik Chattopadhyay .

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Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Das, D., Agarwal, A., Chattopadhyay, P. (2023). Gait Recognition from Occluded Sequences in Surveillance Sites. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_47

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  • DOI: https://doi.org/10.1007/978-3-031-25072-9_47

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