多尺度融合的双分支特征提取人群计数算法OA北大核心CSTPCD

Crowd counting has important applications in public safety management,public space design,and other visual tasks such as behavior analysis and congestion analysis.However,the complexity of the background and the varying size of the head scale result in unsatisfactory crowd counting performance.To address the issues of scale changes and back-ground interference in static images,a crowd counting network based on dual branch intermediate feature extraction is proposed.The network follows the encoder decoder structure and uses the first 16 layers of VGG19 convolutional neural network in the encoding stage.In order to better fuse multi-scale information,it replaces the last 4 convolutions of the first 16 layers of the VGG19 convolutional neural network with dilated convolutions with a vacancy rate of 2.The decoding part uses a residual convolutional attention module(RCAM)to suppress background interference,and inserts a dual branch intermediate feature extraction module(DBFE)in the middle of the encoder decoder structure.Branch 1 adopts a pyramid structure and integrates the position attention module to extract multi-scale contextual information,branch 2 fol-lows a pyramid structure and integrates a dual channel attention mechanism to focus the model on different sizes of head information,and finally uses 1×1 generate density maps through convolution.In terms of experiments,algorithm compari-son experiments are carried out on the data sets of ShanghaiTech PartA,ShanghaiTech PartB and Mall.The average abso-lute error and root mean square error of the model in the above data sets are 63.2,7.1,1.80 and 99.2,11.8,2.28,respectively.Through comparative experimental analysis,the model has good counting performance and stability.Ablation experi-ments are conducted on ShanghaiTech PartB,which verifies the effectiveness of each module of the model.