Gait recognition for person re-identification

Elharrouss, Omar, Almaadeed, Noor, Al-Maadeed, Somaya and Bouridane, Ahmed (2021) Gait recognition for person re-identification. The Journal of Supercomputing, 77 (4). pp. 3653-3672. ISSN 0920-8542

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Official URL: https://doi.org/10.1007/s11227-020-03409-5

Abstract

Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

Item Type: Article
Uncontrolled Keywords: Gait recognition, Angle estimation, Motion detection, Convolutional neural networks
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 06 May 2021 12:35
Last Modified: 31 May 2021 14:38
URI: http://nrl.northumbria.ac.uk/id/eprint/46110

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