[1]GAO Shangbing,HUANG Zihe,GENG Xuan,et al.A visual collaborative analysis method for detecting illegal driving behavior[J].CAAI Transactions on Intelligent Systems,2021,16(6):1158-1165.[doi:10.11992/tis.202101024]
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A visual collaborative analysis method for detecting illegal driving behavior

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1158-1165 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-11-05
Title:
A visual collaborative analysis method for detecting illegal driving behavior
Author(s):
GAO Shangbing1; 2;  HUANG Zihe1;  GENG Xuan1;  ZANG Chen1;  Shen Xiaokun1
1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;
2. Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaian 223001, China
Keywords:
driving behavior recognition; model pruning; target detection; attitude estimation; collaborative detection; model optimization; deep learning; convolutional neural network
CLC:
TP391.4
DOI:
10.11992/tis.202101024
Abstract:
This study proposes a fast and reliable visual collaborative analysis method to improve the reliability of mainstream behavior detection algorithms in dangerous driving recognition. First, the algorithm performs target detection on sensitive objects such as mobile phones, water cups, and cigarettes. The proposed low weight-Yolov4 algorithm improves the detection speed by removing unimportant element channels in the cross-stage partial Darknet53 convolutional layer and regularizes L1 to generate a sparse weight matrix. Besides, the obtained matrix is added to the gradient of the batch normalization layer to optimize the network model. Then, an attitude detection algorithm is used to detect key points of the driver’s knuckles, and the coordinates in the original frame are obtained through the affine inverse transformation. Finally, the driver’s illegal driving behavior and its category are determined through visual collaborative analysis and comparison of the position of the detection frame of sensitive objects and coordinates of the driver’s hands. Experimental results show that the recognition accuracy and detection speed of the proposed method are better than those of mainstream algorithms, which can meet the detection requirements of real-time and reliability.
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Last Update: 2021-12-25

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