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. The system uses a YOLO-based pose estimator to extract body keypoints for each tracked person and computes kinematic and interaction features describing hand speed, arm extension, proximity, and relative motion between an aggressor-victim pair. A Random Forest classifier is trained on these descriptors, and a temporal hysteresis filter is applied to stabilize frame-level predictions and reduce spurious alarms. We evaluate the method on a staged dataset and on a disjoint test set collected from internet videos, demonstrating promising generalization across different scenes and camera viewpoints. Finally, we implement the complete pipeline on an NVIDIA Jetson Nano and report real-time performance, supporting the feasibility of proactive, on-device robbery detection.
@article{arxiv.2604.14329,
title = {Interpretable Human Activity Recognition for Subtle Robbery Detection in Surveillance Videos},
author = {Bryan Jhoan Cazáres Leyva and Ulises Gachuz Davila and José Juan González Fonseca and Juan Irving Vasquez and Vanessa A. Camacho-Vázquez and Sergio Isahí Garrido-Castañeda},
journal= {arXiv preprint arXiv:2604.14329},
year = {2026}
}