基于双关键点的拥挤行人检测方法  

Pedestrian detection in crowd based on dual key points

作　　者：沈继锋[1] 盛常宝 陈逸飞 左欣[2] SHEN Jifeng;SHENG Changbao;CHEN Yifei;ZUO Xin(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013 [2]江苏科技大学计算机学院,江苏镇江212003

出　　处：《江苏大学学报(自然科学版)》2025年第2期140-148,共9页Journal of Jiangsu University:Natural Science Edition

基　　金：国家自然科学基金资助项目(61903164);江苏省自然科学基金资助项目(BK20191427)。

摘　　要：针对行人检测中远距离目标像素稀少和遮挡产生人体模式信息缺失导致的严重漏检问题,提出一种基于双关键点组合的行人检测方法.该方法利用人体头部与中心区域的关键点,有效提取和融合行人的判别语义特征,从而显著降低行人的漏检率.首先,在深层聚合主干特征网络上引入可变形卷积来扩大感受野,增强人体模式的语义信息;其次,设计了一种基于关键点组合的双分支联合检测模块,通过重新定义不同分支的正样本,强化小尺度与遮挡目标的语义信息;最后,借助非极大值抑制算法融合双分支检测结果.结果表明:在CityPerson验证数据集的普通、小尺度与严重遮挡子集上,文中方法的平均漏检率分别达到8.24%、11.81%和30.59%,特别是对于严重遮挡子集,漏检率相比传统方法ACSP降低15.71%;文中方法检测速度也达到16帧/s;在CrowdHuman上文中方法的平均精度和平均漏检率分别达到86.30%和45.52%.与其他先进方法相比,文中方法在平均精度、漏检率和检测速度方面都呈现出更优异的性能,在密集行人的复杂场景中具有较好的应用价值.To solve the severe miss detection problem in pedestrian detection caused by insufficient pixel information of distant targets and occlusion-induced loss of human pattern information,the pedestrian detection method was proposed based on dual key points combinations.The discriminative semantic features of pedestrians were effectively extracted and fused by utilizing key points of the head and center regions for significantly reducing the pedestrian miss detection rate.The deformable convolution was introduced into the deep aggregation backbone feature network to enlarge the receptive field and enhance the semantic information of human pattern.The dual-branch joint detection module based on key points combinations was designed,and the positive samples for different branches were redefined to strengthen the semantic information of small-scale and occluded targets.The results of the dual-branch detection were fused using the non-maximum suppression(NMS) algorithm.The results show that on the CityPerson validation dataset,the average miss detection rates of the normal,small-scale and heavily occluded subsets reach 8.24%,11.81% and 30.59%,respectively.Especially,for the heavily occluded subset,the miss detection rate is reduced by 15.71% compared to the traditional method ACSP.By the proposed method,the detection speed reaches 16 frames per second.On the CrowdHuman dataset,the average precision and average miss detection rate are 86.30% and 45.52%,respectively.Compared with other state-of-the-art methods,the proposed method exhibits superior performance in average precision,miss detection rate and detection speed,which demonstrates significant application value in complex scenarios with dense pedestrian crowds.

关 键 词：行人检测 拥挤场景 遮挡目标 小尺度目标 双关键点 可变形卷积 双分支融合 非极大值抑制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]