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Video Surveillance Detection Method Based on Improved YOLOv3 algorithm and Human Body Information Data Fusion
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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 01 Page number: 28-34 Column: Public date: 2021-03-14
Title:
Video Surveillance Detection Method Based on Improved YOLOv3 algorithm and Human Body Information Data Fusion
Author(s):
ZHANG Zhen;  LI Haofang;  LI Mengzhou;  MA Junqiang
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Keywords:
video surveillance;  K-means++;  GIoU;  multi-scale training;  improved YOLOv3;  human information
CLC:
TP391.41
DOI:
10.13705/j.issn.1671-6833.2021.01.005
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.
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Last Update: 2021-03-15
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