Mohammed Alnusayri1, Tingting Xue2,3, Saleha Kamal4, Nouf Abdullah Almujally5, Khaled Alnowaiser6, Ahmad Jalal4,7,*, Hui Liu3,8,9,*
CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074115
- 08 May 2026
Abstract Group activity recognition in public environments is challenging due to dynamic formations, complex inter-person interactions, and frequent occlusions. Existing methods often emphasize individual actions, overlooking collective behavioral patterns. This work introduces a multi-modal framework integrating silhouette-based appearance and skeleton-based pose information for robust recognition in surveillance scenarios. You Only Look Once v11 (YOLOv11) detects persons, Segmenting Objects by LOcations version 2 (SOLOv2) segments instances, and AlphaPose extracts skeletons, followed by hierarchical grouping to form spatially coherent clusters. A hybrid feature extraction strategy combines handcrafted descriptors (Extended GIST (ExGIST), Distance Transform, Binary Robust Independent Elementary Features More >