[1]熊群芳,林 军,岳 伟,等.基于深度学习的驾驶员打电话行为检测方法[J].控制与信息技术,2019,(06):53-56.[doi:10.13889/j.issn.2096-5427.2019.06.400]
 XIONG Qunfang,LIN Jun,YUE Wei,et al.A Driver’s Calling Behavior Detection Method Based on Deep Learning[J].High Power Converter Technology,2019,(06):53-56.[doi:10.13889/j.issn.2096-5427.2019.06.400]
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基于深度学习的驾驶员打电话行为检测方法()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年06期
页码:
53-56
栏目:
人工智能技术与应用
出版日期:
2019-12-05

文章信息/Info

Title:
A Driver’s Calling Behavior Detection Method Based on Deep Learning
文章编号:
2096-5427(2019)06-0053-04
作者:
熊群芳1林 军1岳 伟1刘世望2罗 潇1丁 驰1
(1.中车株洲电力机车研究所有限公司,湖南株洲 412001;2. 西南交通大学电气工程学院,四川 成都 611756)
Author(s):
XIONG Qunfang1 LIN Jun1 YUE Wei1 LIU Shiwang2 LUO Xiao1 DING Chi1
( 1.CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China;2.School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 611756, China )
关键词:
深度学习卷积神经网络人脸检测打电话行为检测
Keywords:
deep learning convolutional neural network face detection cell phone usage detection
分类号:
TP399
DOI:
10.13889/j.issn.2096-5427.2019.06.400
文献标志码:
A
摘要:
为避免驾驶员因使用手机而无法对突发事故做出及时处理现象的发生,通过视频分析技术对驾驶员行为进行实时监控变得尤为重要。针对目前已有检测方法因存在异物遮掩、图像旋转、光照变化及难以提取图像深层特征等缺点而导致检测精度较低的问题,文章提出了一种基于深度学习的驾驶员打电话行为检测方法:首先采用渐进校准网络(progressive calibration networks,PCN)算法实现人脸检测及实时跟踪,从而确定打电话检测候选区域;然后采用基于卷积神经网络算法在候选区域实现驾驶员打电话行为检测。实际场景驾驶检测结果表明,本文所提方法不仅鲁棒性高,而且精度达到96.56%,误检率为1.52%,处理速度达到25帧/s,可以有效地进行驾驶员打电话行为检查监测。
Abstract:
In order to prevent the driver from being distracted by the cell phone call, real-time monitoring of drivers’ behavior through video analysis is especially important. At present, driver’s calling behavior detection methods are prone to object occlusion, image rotation, illumination change and are difficult to extract deep features of the image, which degrade the detection accuracy. This paper proposed a driver’s cell phone calling behavior detection algorithm based on deep learning. The algorithm comprises two steps. Firstly, face detection and face tracking is supported by PCN (progressive calibration networks) to determine the calling detection area. Secondly, the driver’s cell phone calling behavior detection method based on convolutional neural network is used to detect the cell phone in the candidate area. Experimental test results show that the proposed algorithm has high robutness, and its accuracy reaches 96.56%, the false positive rate reaches 1.52%, and the processing speed reaches 25 frames per second. It can effectively detect the driver’s calling behavion.

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备注/Memo

备注/Memo:
收稿日期:2019-09-18
作者简介:熊群芳(1990—),女,硕士研究生,研究方向为图像处理。
基金项目:国家重点研发计划(2018YFB1201600)
更新日期/Last Update: 2019-12-25