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JACIII Vol.24 No.5 pp. 638-647
doi: 10.20965/jaciii.2020.p0638
(2020)

Paper:

Projection with Gaussian Kernel for Person Re-Identification

Dao Nam Anh*1, Thuy-Binh Nguyen*2,*3,*4, and Thi-Lan Le*2,*3

*1Faculty of Information Technology, Electric Power University
235 Hoang Quoc Viet Road, Hanoi, Vietnam

*2International Research Institute Multimedia, Information, Communication and Applications, Hanoi University of Science and Technology
No.1 Dai Co Viet Street, Hai Ba Trung District, Hanoi, Vietnam

*3School of Electronics and Telecommunications, Hanoi University of Science and Technology
No 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

*4Faculty of Electrical-Electronic Engineering, University of Transport and Communications
No.3 Cau Giay Street, Lang Thuong ward, Dong Da District, Hanoi, Vietnam

Received:
March 9, 2020
Accepted:
May 24, 2020
Published:
September 20, 2020
Keywords:
person re-identification, manifold, Gaussian kernel
Abstract
Projection with Gaussian Kernel for Person Re-Identification

The framework for manifold person ReID

Person re-identification (ReID), the task of associating the detected images of a person as he/she moves in a non-overlapping camera network, is faced with different challenges including variations in the illumination, view-point and occlusion. To ensure good performance for person ReID, the state-of-the-art methods have leveraged different characteristics for person representation. As a result, a high-dimensional feature vector is extracted and used in the person matching step. However, each feature plays a specific role for distinguishing one person from the others. This paper proposes a method for person ReID wherein the correspondences between descriptors in high-dimensional space can be achieved via explicit feature selection and appropriate projection with a Gaussian kernel. The advantage of the proposed method is that it allows simultaneous matching of the descriptors while preserving the local geometry of the manifolds. Different experiments were conducted on both single-shot and multi-shot person ReID datasets. The experimental results demonstrates that the proposed method outperforms the state-of-the-art methods.

Cite this article as:
D. Anh, T. Nguyen, and T. Le, “Projection with Gaussian Kernel for Person Re-Identification,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 638-647, 2020.
Data files:
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Last updated on Dec. 03, 2020