AI-Driven Abnormal Behaviour Detection within Crowds

Abnormal behaviour detection is crucial given the reliance on video surveillance for public safety. However, most vision-based methods fail to adapt to the variability and complexity of the crowd scenes inherent in real-world scenarios. This study introduced an abnormal crowd behaviour detection algorithm based on lightweight Graphic Neural Networks (GNNs) using trajectory similarities and spatial-temporal attributes. Integrating Graph Attention Networks (GATs) to encode the graph structure, creating latent embeddings that capture both local and global relationships within crowds. Also introducing an autoencoder (AE) structure to identify anomalies by comparing true movement with the decoded output. Anomaly is detected when the reconstruction error is high. Experiments on UCSD Pedestrians and ShanghaiTech campus datasets show the successful use of trajectory similarities as graph features for GNNs to generate logical embeddings of crowd movement and highlight the impact of time windows on anomaly detection precision as reflected in F1-scores. The Area Under the Curve (AUC) metric shows stability, highlighting the model’s balanced performance. Qualitative analysis of the ShanghaiTech campus dataset illustrates the model’s ability to align detection with visual anomalies. Overall, this study demonstrates the effectiveness of the proposed graph-based anomaly detection framework for crowd monitoring through a lightweight model suitable for CCTV.