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Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)

Nayak, GK and Shreemali, U and Babu, RV and Chakraborty, A (2019) Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). In: 26th IEEE International Conference on Image Processing, ICIP 2019, 22 - 25 September 2019, Taipei, pp. 4569-4573.

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Official URL: https://doi.org/10.1109/ICIP.2019.8803674


Given a sequence of observations for each person in each camera, identifying or re-identifying the same person across different cameras is one of the objectives of video surveillance systems. In the case of video based person re-id, the challenge is to handle the high correlation between temporally adjacent frames. The presence of non-informative frames results in high redundancy which needs to be removed for an efficient re-id. We propose a novel method to handle this challenge using Determinantal Point Process (DPP) to select the most diverse and informative subset of frames from a given sequence. Since subset selection problem is NP-Hard, we propose to use an approximate solution called Lazy Greedy DPP (LGDPP) and further extend it to utilize the temporal information of sequences with our proposed Sequential LGDPP (SLGDPP) for video-based person re-id. The major advantages of the proposed DPP variants are their simplicity and plug and play nature, which make it possible to use them atop any pretrained re-id model followed by a feature fusion module. The effectiveness of proposed frameworks is demonstrated on two popular video re-id benchmark datasets through improvements over state-of-the-art methods and naive baseline sampling methods. © 2019 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Conference on Image Processing, ICIP
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society.
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 06 Jan 2023 06:32
Last Modified: 06 Jan 2023 06:32
URI: https://eprints.iisc.ac.in/id/eprint/78811

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