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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Zhihui Li

Beijing Etrol Technologies Co., Ltd.


Lina Yao

University of New South Wales


Feiping Nie

Northwestern Polytechnical University


Dingwen Zhang

Northwestern Polytechnical University


Min Xu

University of Technology Sydney


DOI:

10.1609/aaai.v32i1.12302


Abstract:

Matching pedestrians across multiple camera views has attracted lots of recent research attention due to its apparent importance in surveillance and security applications.While most existing works address this problem in a still-image setting, we consider the more informative and challenging video-based person re-identification problem, where a video of a pedestrian as seen in one camera needs to be matched to a gallery of videos captured by other non-overlapping cameras. We employ a convolutional network to extract the appearance and motion features from raw video sequences, and then feed them into a multi-rate recurrent network to exploit the temporal correlations, and more importantly, to take into account the fact that pedestrians, sometimes even the same pedestrian, move in different speeds across different camera views. The combined network is trained in an end-to-end fashion, and we further propose an initialization strategy via context reconstruction to largely improve the performance. We conduct extensive experiments on the iLIDS-VID and PRID-2011 datasets, and our experimental results confirm the effectiveness and the generalization ability of our model.

Topics: AAAI

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HOW TO CITE:

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification AAAI 2018, .

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu (2018). Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. 2018. Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. (2018) "Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu, "Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification", AAAI, p., 2018.

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. "Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. "Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Zhihui Li||Lina Yao||Feiping Nie||Dingwen Zhang||Min Xu. Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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