高玮军;师阳;杨杰;张春霞;

• 1:兰州理工大学计算机与通信学院

摘要(Abstract)：

为了提高视频监控中人数统计的精度和速度,解决传统人体检测由于衣物身体阻挡而造成的高遮挡问题。提出一种改进的轻量人头检测方法 MKYOLOv3-tiny。该方法是对YOLOv3-tiny进行改进,针对低层的人头特征进行多尺度融合,实现不同卷积层的分类预测与位置回归,提升检测的精度;针对人头较小的特点,结合有效感受野的思想,K-means聚类减小初始候选框的规格,提升候选框的精度。实验结果表明,改进后的模型在Brainwash密集人头检测数据集上与原方法相比,在精度上提升了3.21%,漏检率降低了8.7%。

关键词(KeyWords)： 人头检测;多尺度融合;K-means;有效感受野;密集人数统计

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（61762059）;; 甘肃省引导创新发展项目（062004）

作者(Author): 高玮军;师阳;杨杰;张春霞;

Email:

DOI:

参考文献(References)：

• [1] VORA A,CHILAKA V.FCHD:Fast and accurate head detection in crowded scenes[J].arXiv:1809.08766,2018.
• [2] ZHANG Y,ZHOU D,CHEN S,et al.Single-image crowd counting via multi-column convolutional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:589-597.
• [3] LI Y,ZHANG X,CHEN D.Csrnet:Dilated convolutional neural networks for understanding the highly congested scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:1091-1100.
• [4] JIANG X,XIAO Z,ZHANG B,et al.Crowd counting and density estimation by trellis encoder-decoder networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2019:6133-6142.
• [5] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2014:580-587.
• [6] GIRSHICK R.Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision,2015:1440-1448.
• [7] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2017,39(6):1137-1149.
• [8] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision,2016:21-37.
• [9] REDMON J,DIVVALA S K,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the 29th IEEE Computer Vision and Pattern Recognition,2016:779-788.
• [10] GAO C,LIU J,FENG Q,et al.People-flow counting in complex environments by combining depth and color information[J].Multimedia Tools and Applications,2016,75:9315-9331.
• [11] LUO J,WANG J,XU H,et al.Real-time people counting for indoor scenes[J].Signal Processing,2016,124:27-35.
• [12] VU T H,OSOKIN A,LAPTEV I.Context-aware CNNs for person head detection[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:2893-2901.
• [13] PENG D,SUN Z,CHEN Z,et al.Detecting heads using feature refine net and cascaded multi-scale architecture[C]//Proceedings of the 24th International Conference on Pattern Recognition,2018.
• [14] CHI C,ZHANG S,XING J,et al.Relational learning for joint head and human detection[J].ar Xiv:1909.10674,2019.
• [15]高宗，李少波，陈济楠，等.基于YOLO网络的行人检测方法[J].计算机工程，2018,44(5):215-219.
• [16]葛雯，史正伟.改进YOLOV3算法在行人识别中的应用[J].计算机工程与应用，2019,55(20):128-133.
• [17]徐晓涛，孙亚东，章军.基于YOLO框架的血细胞自动计数研究[J].计算机工程与应用，2020,56(14):98-103.
• [18] WAGSTAFF K,CARDIE C,ROGERS S,et al.Constrained k-means clustering with background knowledge[C]//Proceedings of the 18th International Conference on Machine Learning,2001:577-584.
• [19] REDMON J,FARHADI A.YOLOv3:An incremental improvement[J].arXiv:1804.02767,2018.
• [20]赵亚男，吴黎明，陈琦.基于多尺度融合SSD的小目标检测方法[J].计算机工程，2020,46(1):247-254.