[1]张震,李浩方,李孟洲,等.改进YOLOv3算法与人体信息数据融合的视频监控检测方法[J].郑州大学学报(工学版),2021,42(01):28-34.[doi:10.13705/j.issn.1671-6833.2021.01.005]
　ZHANG Zhen,LI Haofang,LI Mengzhou,et al.Video Surveillance Detection Method Based on Improved YOLOv3 algorithm and Human Body Information Data Fusion[J].Journal of Zhengzhou University (Engineering Science),2021,42(01):28-34.[doi:10.13705/j.issn.1671-6833.2021.01.005]
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改进YOLOv3算法与人体信息数据融合的视频监控检测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年01期
页码:
28-34
栏目:
出版日期:
2021-03-14

文章信息/Info

Title:
Video Surveillance Detection Method Based on Improved YOLOv3 algorithm and Human Body Information Data Fusion
作者:
张震; 李浩方; 李孟洲; 马军强
郑州大学电气工程学院;

Author(s):
ZHANG Zhen;  LI Haofang;  LI Mengzhou;  MA Junqiang
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
Keywords:
video surveillance;  K-means++;  GIoU;  multi-scale training;  improved YOLOv3;  human information
分类号:
TP391.41
DOI:
10.13705/j.issn.1671-6833.2021.01.005
文献标志码:
A
摘要:
针对目前社区视频监控使用人脸相机仅采集出入口人脸数据，而缺失有数据价值的人体其它信息的问题. 本文提出一种将改进YOLOv3网络和调用人体信息识别模块相结合的人体信息检测方法.采用K-means++算法获取数据集的先验框，选用新的边界框回归损失函数GIoU提高检测精度，再进行多尺度训练得到人体检测网络模型，最后利用人体检测模型在检测到人体目标后调用人体信息识别模块对人体信息进行分析和保存.实验结果表明，该方法既能快速检测人体目标，还能准确获取人体目标的各种属性信息.其中人体检测模型在测试集上的mAP（Mean Average Precision）达为91.8%，识别速率为45帧/s
Abstract:
In the current community video surveillance system, only a face camera was used to collect the entrance and exit face data, other valuable human information was negelected. In this paper, a human information detection method that combined improved YOLOv3 network and calling human information recognition module was proposed. The K-means++ algorithm was used to obtain the prior frame of the data set; the new bounding box regression loss function GIoU was used to improve the detection accuracy, and then multi-scale training was performed to obtain the human detection network model. Finally, the human detection model was used to detect human targets; and the human body information recognition module was used to analyze and save human body information. The experimental results showed that the method could detect human targets quickly, and accurately obtain various attribute information of human targets. Among them, the mAP of human detection model on the test set reached 91.8%, and the recognition speed was 45 f/s.

参考文献/References:

[1] 赵思阳. 社区治理现代化视域下智慧社区建设研究——以洛阳市涧西区天津路街道为例[D].郑州: 郑州大学, 2018.

[2] GIRSHICK R,DONAHUE J,DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE,2014:580-587.
[3] GIRSHICK R. Fast R-CNN [C]//2015 IEEE International Conference on Computer Vision. New York:IEEE, 2015: 1440-1448.
[4] 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 and machine intelligence, 2017,39(6): 1137-1149.
[5] DAI J F,LI Y, HE K M, et al. R-FCN: object detection via region-based fully convolutional networks[J]. Computer vision and pattern recognition, 2016, 29: 379-387.
[6] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York：IEEE,2016: 779-788.
[7] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector [M]//LEIBE B, MATAS J, SEBE N, et al.Computer Vision-ECCV 2016. Cham: Springer International Publishing,2016:21-37.
[8] REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).New York:IEEE,2017:6517-6525.
[9] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D,et al. Object detection with discriminatively trained part-based models [J]. IEEE transactions on pattern analysis & machine intelligence, 2010, 32(9): 1627-1645.
[10] 张素洁,赵怀慈. 最优聚类个数和初始聚类中心点选取算法研究[J]. 计算机应用研究, 2017, 34(6): 1617-1620.
[11] REZATOFIGHI H,TSOI N,GWAK J,et al.Generalized intersection over union: a metric and a loss for bounding box regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).New York:IEEE,2019:658-666.
[12] 施辉,陈先桥,杨英. 改进YOLOv3的安全帽佩戴检测方法[J]. 计算机工程与应用, 2019, 55(11): 213-220.
[13] LIN T Y, DOLLwidth=9,height=12,dpi=110R P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE,2017:936-944.
[14] LIU W, WEN Y, YU Z, et al. Large-margin softmax loss for convolutional neural networks[C]//Procee-dings of the 33rd International Conference on Machine Learning. Washington DC:IMLS, 2016: 507-516.
[15] HE K M,ZHANG X Y,REN S Q,et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770- 778.
[16] 魏宏彬,张端金,杜广明,等. 基于改进型YOLO v3的蔬菜识别算法[J].郑州大学学报(工学版),2020,41(2):7-12.

更新日期/Last Update: 2021-03-15