Among the abnormal behavior detection methods, it is difficult to describe the crowd behavior. For this case, an abnormal behavior detection approach based on behavioral similarity (collectiveness features) between individual and other individuals in the group is proposed. Firstly, Gaussians mixture model was used to extract the background of the video. Then, Kanade-Lucas-Tomasi (KLT) algorithm was used to track the moving crowd. Next, collectiveness features integrated the motion information of the whole crowd are extracted from the direction and speed of the crowd movement. Finally, a histogram derived from the collectiveness features was defined to measure the anomaly of crowd activity, and the entropy of the histogram was computed to recognize abnormal events. Experiments were conducted on various video datasets, and results were presented to verify the effectiveness of the proposed scheme.