Research And Implementation Of Crowd Count Algorithm Based On Spatial Information

In recent years,crowd activities lead to crowd aggregation.The aggregation of people will produce risks.Once this risk encounters some special events,it may cause some malignant events.As one of the important reference standards of flow control,crowd count plays an important role in drainage.The traditional way of management is to monitor the video image manually.This way requires the operator to focus on observing the changes of the monitoring screen.Relying on the intelligent monitoring platform,real-time crowd count and crowd distribution can be obtained from the monitoring video or image.Intelligent judgment and early warning are of significance to security work.The complexity of crowd scene is caused by camera angle,crowd scale change,occlusion and background noise.How to generate accurate crowd density map and count is a difficult problem.In this thesis,a new algorithm of crowd counting is proposed,and its feasibility is verified by experiments.The main work of this thesis is as follows:1.Crowd counting based on dense sub-pixel convolution is proposed.It is difficult to capture the scale change in previous method,this thesis based on dense connection fusion multi-scale information.Based on the features extracted from the basic feature extraction network,dense connection is used for multi-scale feature fusion.The previous method uses interpolation method for upsampling,which can't adaptively restore the scale.A sub-pixel convolution based upsampling method is proposed to fill the dimension information of the feature map with the information of the channel.The model is compared with some classical algorithms to verify the effectiveness of the scheme.2.Crowd counting based on fusion of regional location information and density information.In view of the problem that the noise is predicted as the crowd,a scheme of fusion of regional location information and density information is proposed.In order to locate the object area,the crowd area is determined based on the threshold segmentation algorithm and the adversarial earse mechanism.According to the different importance of each part of the image,based on the attention mechanism,the regional location information and the predicted density information are fused.Improve the density map and eliminate the counting error caused by noise.Finally,the performance of the algorithm is evaluated by qualitative and quantitative analysis3.The implementation of crowd counting application system.Based on the investigation of the demand of real scene,a crowd counting system is designed and implemented.By visiting the algorithm service designed in this thesis,the density map generation and crowd counting are finished,and then the subsequent statistical and judgment work is completed.The application system provided can meet the application needs of most monitoring related aspects,and can realize intelligent judgement.