基于多尺度时间递归神经网络的人群异常检测

如何在人群密度大、变化快、存在大量遮挡的密集场景中实现可靠的人群事件检测,是领域研究的难点和热点.在密集场景时空建模的基础上提出了一种基于多尺度时间递归神经网络的人群异常事件检测和定位方法.首先对人群场景进行网格化划分,并利用多尺度光流直方图对每个网格的人群动态进行刻画;然后,连接各个局部的人群动态获得整体的人群动态,实现整体人群动态的时间序列建模;最后,利用多尺度时间递归神经网络实现异常事件的检测和定位.其中,多尺度隐含层实现了密集场景中不同规模相邻网格之间的空间联系,节点间的反馈关系则为时间维度上的关系表达提供了有效方案.与多种代表性算法的对比实验,验证了本方法的有效性.

Abnormal Crowd Detection Based on Multi-Scale Recurrent Neural Network

Because of the great variations of crowd density and crowd dynamics, as well as the existence of many shelters in scenes, the abnormal crowd event detection and localization are still challenging problems and hot topics of the crowd scene analysis. Based on the spatial-temporal modeling of the crowd scene, this paper proposes an abnormal crowd event detection and localization approach based on multi-scale recurrent neural network. Firstly, the crowd scenes are split into grids and presented using multi-scale histogram of optical flow (MHOF). Then, different grids are connected to obtain a global time series model of the crowd scene. Finally, a multi-scale recurrent neural network is devised to detect and locate the abnormal event on the time series model of the crowd scene. In the multi-scale recurrent neural network, the multi-scale hidden layers are used to model the spatial relation among different scale neighbors, and the feedback loops are used to catch the temporal relation. Extensive experiments demonstrate the effectiveness of the presented approach.