基于多通道耦合的时空增强异常行为检测OA

To address the limitations of existing abnormal behavior detection models—particularly their inadequate feature representation and insufficient modeling of dynamic temporal characteristics—this paper proposes a multi-channel coupled spatio-temporal enhanced anomaly detection method.Built upon the SlowFast network architecture,the proposed approach integrates a multi-channel coupled spatial enhancement module into the slow pathway to strengthen static feature modeling,and a multi-channel coupled temporal enhancement module into the fast pathway to improve the discriminability of dynamic temporal features.Extensive experiments on three benchmark datasets—Violent Flow,Hockey Fight,and Real-life Violence Situations—demonstrate that the proposed method achieves prediction accuracies of 95.3%,97.3%,and 94%,respectively,outperforming current state-of-the-art approaches.The results validate the superior feature representation capability and generalization performance of the proposed method in abnormal behavior recognition tasks.