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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2019

07.06.2018 | Original Article

Simultaneous visual-appearance-level and spatial-temporal-level dictionary learning for video-based person re-identification

verfasst von: Xiaoke Zhu, Xiao-Yuan Jing, Fei Ma, Li Cheng, Yilin Ren

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

Person re-identification (re-id) plays an important role in video surveillance and forensics applications. In many cases, person re-id should be conducted between video clips, i.e., given a query pedestrian video from one camera, the re-id system should retrieve the video clips containing the same person from other cameras. However, person re-id between videos, which we call video-based person re-id, has not been well studied. In this paper, we propose a visual-appearance-level and spatial-temporal-level dictionary learning (VSDL) approach for video-based person re-id. Specifically, we first employ two kinds of models to represent each walking cycle in the video, i.e., visual-appearance features of all frames within the walking cycle, and a spatial-temporal feature vector. By separately learning a visual-appearance-level dictionary and a spatial-temporal-level dictionary from two kinds of representations, each walking cycle can be represented as a coding coefficient. To enhance the discriminative ability of the obtained coding coefficients, we design a representation coefficient discriminant term for VSDL. Experiments on the public iLIDS-VID and PRID 2011 datasets demonstrate the effectiveness of VSDL.
Vorheriger Artikel A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites optimization
Nächster Artikel A hybrid social influence model for pedestrian motion segmentation

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Metadaten
Titel
Simultaneous visual-appearance-level and spatial-temporal-level dictionary learning for video-based person re-identification
verfasst von
Xiaoke Zhu
Xiao-Yuan Jing
Fei Ma
Li Cheng
Yilin Ren
Publikationsdatum
07.06.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3529-7

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