Abstract: There are few methods for detecting abnormal behaviors of crowd massing in public places, and they have the following problem: most of the detection methods are performed after the crowd has gathered abnormally, and the detection accuracy is not high, and the timeliness is not good enough. Therefore, a crowd massing anomaly prediction model based on 
multi-scale convolutional neural network (MCNN) is proposed. Firstly, a crowd counting model is built through MCNN for testing the video of crowd massing anomaly. In the test, the number of crowd and the coordinate points of their heads are acquired. Secondly, the crowd density, crowd distance potential energy and crowd distribution entropy are calculated. Finally, the predictive model is built through the eigenvalues of three crowd motion state by PSO-ELM. Through the change of characteristic data, the prediction is completed. The experimental results show that, compared with the existing algorithms, the proposed algorithm can effectively achieve the early warning and detection of abnormal behaviors in crowd massing. With a prediction accuracy rate of 97.17%, it’s more time-sensitive and provides more time for taking corresponding emergency measures.

Key words: prediction of crowd massing abnormity, multi-scale convolutional neural network, crowd motion state, crowd density, crowd distance potential energy, crowd distribution entropy