Unsupervised fast anomaly detection in crowds

In this paper, we proposed a fast and robust unsupervised framework for anomaly detection and localization in crowed scenes. Our method avoids modeling the normal state of the crowds which is a very complex task due to the large within class variance of the normal target appearance and motion patterns. For each video frame, we extract the spatial temporal features of 3D blocks and generate the saliency map using a block-based centersurround difference operator. Then, motion vector matrix is obtained by adaptive rood pattern search block-matching algorithm and distance normalization. Attractive motion disorder descriptor is proposed to measure the global intensity of anomalies in the scene. Finally, we classify the frames into normal and anomalous ones by a binary classifier. In the experiments, we compared our method against several state-of-the-art approaches on UCSD dataset which is a widely used anomaly detection and localization benchmark. As the only unsupervised approach, our method outputs competitive results with near real-time processing speed.