The AI Recognition System is an intelligent platform that builds upon AI recognition technology by incorporating behavioural logic analysis capabilities. Its core function involves analysing multidimensional data to interpret behavioural patterns, intentions, and risks associated with humans or objects.

The technical architecture comprises three layers: The input layer integrates cameras, sensors, and other devices to collect time-series data such as image sequences and motion trajectories. The processing layer first employs CNN/RNN for target recognition, then analyses behavioural coherence and compliance through behavioural feature modelling (e.g., temporal action segmentation) and anomaly detection algorithms. The output layer generates behavioural labels (e.g., ‘unauthorised crossing’), risk levels, and warning signals. Applications focus on security and management: in security monitoring, detecting anomalies like climbing or fighting; in industrial settings, identifying non-compliant operations (e.g., failure to wear safety helmets); in traffic scenarios, assessing behaviours such as jaywalking or queue-jumping. The system relies on high-quality temporal data for training, facing challenges including misjudgements due to occlusions and difficulties adapting to complex scenarios. It is currently being optimised towards real-time analysis and multi-scenario applicability.