Panic-driven event detection from surveillance video stream without track and motion features

Modern surveillance systems are becoming highly automated in terms of scene understanding and event detection capabilities, and most existing methods rely on track-and motion-based features for event classification and anomaly detection. However, trajectory-based methods fail in public scenarios due to frequently loosing the object tracks, while the capabilities of motion-based methods are limited in detection of direction and velocity related anomalies. In this paper, a novel feature extraction and event detection method is presented without using any track and motion features where event discriminating characteristics are discovered from the dynamics of multiple temporal features extracted from foreground blobs and then confined in support vector machine based models for real-time event detection. Experimental results on benchmark datasets show that the proposed method can successfully discriminate panic-driven events like sudden split, runaway, and fighting from usual events.