The anomaly in the crowd is a great potential threat, and the automatic detection of abnormal behavior for surveillance has become a hot topic in recent years. However, because the anomaly is unknown and complex, the previous detection methods still suffer from a low detection rate and poor location accuracy. To this end, a method is proposed for anomaly detection and location in the crowded surveillance videos. First, the motion regions are extracted according to the distributions of the gray-scale value and the optical flow field. Second, the effective motion blocks are obtained by segmenting the motion regions. Two features, namely the local H histogram of gradient G and the local H histograms of flow F, are extracted from the motion blocks, representing the appearance and dynamics. Third, the motion blocks are clustered with the k-means method, and each cluster is modeled using a one-class classifiers. Finally, the motion continuity constraint is added to suppress the noisy noises. Experimental results on two complex abnormal behavior datasets show that the proposed method is obviously better than previous detection methods. It could meet the practical engineering needs such as high accuracy and strong anti-interference ability.