Abstract
This study proposes a multi-level system for detecting contactless drug distribution transactions. This system integrates behavioural pattern recognition as the primary detection channel, detection of night-time activity spikes as an enhancing module, and facial matching as an additional probabilistic evaluation layer. The system identifies a two-phase structure of covert transactions. Both the courier’s placement of the product and the buyer’s retrieval produce recognizable behavioural sequences at the same geographic location. Signal-to-noise ratio analysis identified a detection threshold at an SNR of approximately 17. It provides a quantitative foundation for camera placement planning. The behavioural pattern recognition pipeline integrates YOLO-Pose for skeleton estimation and employs classical machine learning models, including Random Forest and Gradient Boosting, for temporal action classification. The ST-GCN architecture is considered a future extension pending the availability of a larger annotated dataset.