Quaternion discrete cosine transformation signature analysis in crowd scenes for abnormal event detection

In this paper, an abnormal event detection approach inspired by the saliency attention mechanism of human visual system is presented. Conventionally, statistics-based methods suffer from visual scale, complexity of normal events and insufficiency of training data, for the reason that a normal behavior model established from normal video data is used to detect unusual behaviors with an assumption that anomalies are events with rare appearance. Instead, we make the assumption that anomalies are events that attract human attention. Temporal and spatial anomaly saliency are considered consistently by representing the pixel value in each frame as a quaternion, with weighted components that composed of intensity, contour, motion-speed and motion-direction feature. For each quaternion frame, Quaternion Discrete Cosine Transformation (QDCT) and signature operation are applied. The spatio-temporal anomaly saliency map is developed by inverse QDCT and Gaussian smoothing. By multi-scale analyzing, abnormal events appear at those areas with high saliency score. Experiments on typical datasets show that our method can achieve high accuracy results.