Detecting Anomalous Crowd Behaviour with Optical Flow and Energy-Based Methods.

In the domain of intelligent surveillance for public safety, rapid anomaly detection in crowded environments is essential. This study presents an approach to crowd behaviour analysis by measuring crowd energy changes. Image pixels are modeled as particles, and optical flow techniques are used to extract velocity vectors and directions. To mitigate the noise, occlusions, and lighting challenges of optical flow, the system incorporates pixel motion estimation across frames, improving temporal coherence for smoother motion. Image grey entropy and Otsu’s segmentation are employed to separate foreground from background, enabling detailed energy distribution analysis. Abnormal crowd activity is detected by observing sudden changes in motion intensity. Evaluation on the UMN dataset shows that the proposed method achieves an accuracy of 96.87% in anomaly detection, outperforming other conventional techniques. These results highlight the improved accuracy and efficiency of the method in detecting anomalous crowd behaviour in complex environments. [ABSTRACT FROM AUTHOR]