Interpretable Human Activity Recognition for Subtle Robbery Detection in Surveillance Videos

ArXi:2604.14329v1 Announce Type: new Non-violent street robberies (snatch-and-run) are difficult to detect automatically because they are brief, subtle, and often indistinguishable from benign human interactions in unconstrained surveillance footage. This paper presents a hybrid, pose-driven approach for detecting snatch-and-run events that combines real-time perception with an interpretable classification stage suitable for edge deployment.