ENHANCING CAMPUS SURVEILLANCE: TEMPORAL SEGMENTATION FOR BEHAVIOR ANALYSIS

Intelligent Video Surveillance systems play a pivotal part in enhancing safety, with abnormal gesture recognition being a crucial element of action recognition in computer vision. While CNN- grounded approaches have been extensively used, landing sufficient stir sequence features remains grueling. This study introduces a new frame that leverages long-term videotape modeling and a global meager livery slice strategy, dividing videos into three equal- duration parts and slightly testing particles. The proposed model employs an agreement of three Temporal Member Mills (TSTs) to establish global patch connections and cipher tone- attention with common spatiotemporal factorization. estimated on the recently developed Lot Abnormal gesture Recognition (CABR50) dataset, containing 50 mortal abnormal action classes with a normal of 700 videotape clips per class, the system demonstrates strong performance in videotape- grounded abnormal gesture recognition. The TST- L model achieves 83.57 Top- 1 delicacy and 97.16 Top- 5 delicacy, proving its effectiveness in lot surveillance and outperforming being videotape recognition ways.