Multi-rate gated recurrent convolutional networks for video-based pedestrian re-identification

Zhihui Li, Lina Yao, Feiping Nie, Dingwen Zhang, Min Xu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationThe Thirty-Second AAAI Conference on Artificial Intelligence
EditorsSheila McIlraith, Kilian Weinberger
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages8
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2018 - New Orleans, United States of America
Duration: 2 Feb 20187 Feb 2018
Conference number: 32nd


ConferenceAAAI Conference on Artificial Intelligence 2018
Abbreviated titleAAAI 2018
Country/TerritoryUnited States of America
CityNew Orleans
Internet address

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