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

Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London Ltd.
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-017-0136-9
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Oct 23, 2017

References

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