@Article{cmes.2025.067743,
AUTHOR = {Zeeshan Ali, Jihoon Moon, Saira Gillani, Sitara Afzal, Maryam Bukhari, Seungmin Rho},
TITLE = {A Region-Aware Deep Learning Model for Dual-Subject Gait Recognition in Occluded Surveillance Scenarios},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {2},
PAGES = {2263--2286},
URL = {http://www.techscience.com/CMES/v144n2/63717},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2025.067743}
}