Crowd Status Analysis Using Optical Flow and Information Entropy

With the frequent occurrence of mass incidents in recent years, the analysis of crowd status and abnormal behavior detection in the crowd scene become the hot topic in the field of computer vision. At present, most of the algorithms can achieve good detection accuracy, but these algorithms have very high complexity and large computation, so it is difficult to guarantee real-time character of the monitor system, such as pure optical flow, social force model and spatial-temporal model. In view of this, the concept of crowd moving area was introduced and the index of crowd status was defined base on crowd moving area to indicate the changes of crowd status. The crowd motion vector field is obtained by optical flow method. The index of crowd motion intensity was defined base on crowd motion vector field to describe the intensity of crowd motion. The index of crowd chaos was defined base on crowd motion vector field and entropy to express the degree of crowd chaos. Furthermore, in order to reduce the computation of algorithm, a hierarchical processing scheme was designed to detect the abnormal crowd behavior. The experimental results show that the method has good effect.