A Region-Aware Deep Learning Model for Dual-Subject Gait Recognition in Occluded Surveillance Scenarios

Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performa...

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Bibliographic Details
Published inComputer modeling in engineering & sciences Vol. 144; no. 2; pp. 2263 - 2286
Main Authors Ali, Zeeshan, Moon, Jihoon, Gillani, Saira, Afzal, Sitara, Bukhari, Maryam, Rho, Seungmin
Format Journal Article
LanguageEnglish
Published Henderson Tech Science Press 2025
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ISSN1526-1506
1526-1492
1526-1506
DOI10.32604/cmes.2025.067743

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Summary:Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions, clothing changes, walking speed, and varying camera viewpoints. Furthermore, most existing research focuses on single-person gait recognition; however, counting, tracking, detecting, and recognizing individuals in dual-subject settings with occlusions remains a challenging task. Therefore, this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios. More precisely, in the proposed method, we have designed a deep learning (DL)-based dual-subject gait model (DSG) involving three modules. The first module handles silhouette segmentation, localization, and counting (SLC) using Mask-RCNN with MobileNetV2. The next stage uses a Convolutional block attention module (CBAM)-based Siamese network for frame-level tracking with a modified gallery setting. Following the last, gait recognition based on region-based deep learning is proposed for dual-subject gait recognition. The proposed method, tested on Shri Mata Vaishno Devi University (SMVDU)-Multi-Gait and Single-Gait datasets, shows strong performance with 94.00% segmentation, 58.36% tracking, and 63.04% gait recognition accuracy in dual-subject walk scenarios.
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ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2025.067743