Anomaly detection in crowded scene via appearance and dynamics joint modeling

In this paper, we propose a novel solution of anomaly detection in crowd scene by jointly modeling appearance and dynamics of motion. First, a novel high-frequency feature based on optical flow (HFOF) is introduced. It can well capture the dynamic information of optical flow. Besides, we adopt the other two types of features, namely multi-scale histogram of optical(MHOF), and dynamic textures (DT). MHOF reserves the motion direction information, while DT captures appearance variant property. The three types of features can complement each other in modeling crowd motions. Finally, multiple kernel learning (MKL) is adopted to train a classifier for anomaly detection. Experiments are conducted on a publicly available dataset of escaping scenarios from University of Minnesota and a challenging dataset from Internet. The results of comparative experiments show the promising performance against other related work.