基于自注意力机制的双分支密集人群计数算法

Dual-branch crowd counting algorithm based onself-attention mechanism

针对密集人群计数中人头尺度变化大、复杂背景干扰的问题,提出基于自注意力机制的双分支密集人群计数算法.该算法结合卷积神经网络(CNN)和Transformer 2种网络框架,通过多尺度CNN分支和基于卷积增强自注意力模块的Transformer分支,分别获取人群局部信息和全局信息.设计双分支注意力融合模块,以具备连续尺度的人群特征提取能力;通过基于混合注意力模块的Transformer网络提取深度特征,进一步区分复杂背景并聚焦人群区域.采用位置级-全监督方式和计数级-弱监督方式,在ShanghaiTech Part A、ShanghaiTech Part B、UCFQNRF、JHU-Crowd++等数据集上进行实验.结果表明,算法在4个数据集上的性能均优于最近研究,全监督算法在上述数据集的平均绝对误差和均方根误差分别为55.3、6.7、82.9、55.7和93.1、9.8、145.1、248.0,可以实现高密集、高遮挡场景下的准确计数.特别是在弱监督算法对比中,以低参数量实现了更佳的计数精度,并达到全监督87.9%的计数效果.

A dual-branch crowd counting algorithm based on self-attention mechanism was proposed to solve the problems of large variation in head scale and complex background interference in crowd counting.The algorithm combined two network frameworks,including convolutional neural network(CNN)and Transformer.The multiscale CNN branch and Transformer branch based on convolution enhanced self-attention module were used to obtain local and global crowd information respectively.The dual-branch attention fusion module was designed to enable continuous-scale crowd feature extraction.The Transformer network with the hybrid attention module was utilized to extract deep features,which facilitated the distinction of complex backgrounds and focused on the crowd regions.The experiments were conducted on ShanghaiTech Part A,ShanghaiTech Part B,UCF-QNRF,JHU-Crowd++and other datasets using position-level full supervision and count-level weak supervision.Results showed that the performance of the proposed algorithms was better than that of recent studies.The MAE and MSE of the fully supervised algorithm in the above datasets were 55.3,6.7,82.9,55.7,and 93.1,9.8,145.1,248.0,respectively,which could achieve accurate counting in high density and high occlusion scenes.Good counting precision was achieved with low parameters,and a counting accuracy of 87.9%of the full supervision was attained especially in the comparison of weakly supervised algorithms.