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International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 4/2017

23.10.2017 | Regular Paper

Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos

verfasst von: P. K. Sathish, S. Balaji

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 4/2017

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Abstract

Automatic re-identification of people entering the camera network is an important and challenging task. Multiple frames of the same person will be easily available in surveillance videos for re-identification. Dealing with pose variations of the person in the image and partial occlusion issues is major challenge in single-frame re-identification process. The use of more frames from the surveillance videos can generate robust descriptor to tackle issues of pose variations and occlusion. In this paper, we have emphasized on using multiple frames from the same video to generate a multi-frame twin-channel descriptor. The work deals with building a spatial-temporal descriptor which takes advantage of the twin paths to extract features of the person image. Mahalanobis distance metric learning algorithms is used for matching and evaluation. Our descriptor is evaluated on two benchmark datasets and found to surpass the performance of the existing methods.
Vorheriger Artikel Fast discrete curvelet transform-based anisotropic feature extraction for biomedical image indexing and retrieval
Nächster Artikel OVIS: ontology video surveillance indexing and retrieval system

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Metadaten
Titel
Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos
verfasst von
P. K. Sathish
S. Balaji
Publikationsdatum
23.10.2017
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 4/2017
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-017-0136-9

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