Title: Person re-identification using kNN classifier-based fusion approach

Authors: E. Poongothai; A. Suruliandi

Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli-627012, Tamilnadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli-627012, Tamilnadu, India

Abstract: Re-identification is the process of identifying the same person from images or videos taken from different cameras. Although many methods have been proposed for re-identification, it is still challenging because of unsolved issues like variation in occlusions, viewpoint, pose and illumination changes. The objective of this paper is, to propose a fusion-based re-identification method to improve the identification accuracy. To meet the objective, texture and colour features are considered. In addition the proposed method employs Mahalanobis metric-based kNN classifier for classification. The performance of proposed method is compared with the existing feature-based re-identification methods. CAVIAR, VIPeR, 3DPes, PRID datasets is used for experiment analysis. Results show that the proposed method outperforms the existing methods. Further it is observed that Mahalanobis metric-based kNN classifier improves the recognition accuracy in re-identification process.

Keywords: person re-identification; colour features; texture feature; feature fusion.

DOI: 10.1504/IJAIP.2020.107009

International Journal of Advanced Intelligence Paradigms, 2020 Vol.16 No.2, pp.113 - 131

Received: 14 May 2016
Accepted: 15 Oct 2016
Published online: 01 May 2020 *

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