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

Title

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

Creator

Sathish P.K.; Balaji S.

Description

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. 2017, Springer-Verlag London Ltd.

Source

International Journal of Multimedia Information Retrieval, Vol-6, No. 4, pp. 289-294.

Date

2017-01-01

Publisher

Springer London

Subject

Cumulative matching curve; Multi-frame descriptor; Person re-identification; Video surveillance

Coverage

Sathish P.K., Department of Computer Science and Engineering, Christ University, Bengaluru, 560074, India; Balaji S., Centre for Incubation, Innovation, Research and Consultancy, Jyothi Institute of Technology, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 21926611

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s13735-017-0136-9

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031902888&doi=10.1007%2fs13735-017-0136-9&partnerID=40&md5=7d5a8906a27a1df5815a26b922b88981