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All for One: Frame-wise Rank Loss for Improving Video-based Person Re-identification

Navaneet, KL and Todi, V and Babu, RV and Chakraborty, A (2019) All for One: Frame-wise Rank Loss for Improving Video-based Person Re-identification. In: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, 12 - 17 May 2019, Brighton, pp. 2472-2476.

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Official URL: https://doi.org/10.1109/ICASSP.2019.8682292


Person re-identification involves retrieving correct matches for a target image (query) from a set of gallery images, while video based re-identification extends this to the case of query and gallery videos. Typical video-based re-id methods ignore the temporal evolution of the intermediate representations of the video sequences. We propose a novel loss function, termed rank loss, to explicitly ensure that the learnt representations achieve enhanced performance and robustness as the sequence progresses and that better intermediate representations result in an improved final representation. Experiments indicate that the addition of rank loss indeed helps in improving the re-id performance while achieving performance comparable to state-of-the-art approaches.

Item Type: Conference Paper
Publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Recurrent neural networks; Speech communication, Attention; Intermediate representations; Person re identifications; Re identifications; Recurrent networks; State-of-the-art approach; Temporal evolution; Video sequences, Audio signal processing
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 30 Nov 2022 06:11
Last Modified: 30 Nov 2022 06:11
URI: https://eprints.iisc.ac.in/id/eprint/78386

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