基于多尺度卷积神经网络的人群聚集异常预测

已有的公共场所人群聚集异常行为检测方法较少，且大多检测方法都是在人群已经异常聚集后再进行检测，检测准确率不高，时效性不够好。提出一种基于多尺度卷积神经网络（MCNN）的人群聚集异常预测模型。首先，通过多尺度卷积神经网络训练一个人群计数模型，用训练好的模型对人群聚集异常视频进行测试；然后在测试中完成人群人数统计与人群头部坐标点获取，进而计算人群密度、人群距离势能与人群分布熵；最后将得到的3种人群运动状态特征值利用PSO-ELM进行训练，得到预测模型，通过特征数据的变化，完成人群聚集行为的预测。实验结果表明，与现有算法相比，该模型能有效实现人群聚集异常行为的预警与检测，时效性强，为采取相应应急措施提供了更多时间，预测准确率达到了9717%。

Prediction of crowd massing abnormity based on multi-scale convolutional neural network

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.