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计算机系统应用:2018,27(11):78-83
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基于多任务CNN的监控视频中异常行人快速检测
李俊杰, 刘成林, 朱明
(中国科学技术大学 信息科学技术学院, 合肥 230027)
Fast Abnormal Pedestrians Detection Based on Multi-Task CNN in Surveillance Video
LI Jun-Jie, LIU Cheng-Lin, ZHU Ming
(School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China)
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投稿时间:2018-03-26    修订日期:2018-04-24
中文摘要: 在近年来社会公共安全受到广泛关注的情况下,如何利用监控视频对异常行人进行监督,预防危险事件的发生成为了一个热门课题.异常行人是指与普通行人在外观上有明显异常性区别的人,例如用头盔大面积遮挡面部或低头躲避摄像头,考虑到异常行人的特征主要集中在头面部,本文提出一种基于多任务卷积神经网络和单类支持向量机的针对头面部特征的异常行人快速检测方法.首先进行头面部区域的检测,然后使用多任务卷积神经网络提取头面部区域的特征,之后使用单类支持向量机判断是正常行人还是异常行人.此外,本文还针对卷积神经网络设计了一种卷积核拆分方法,加快了特征提取的速度,最终实验表明,本文提出的算法能够快速有效的检测出监控视频中的异常行人.
Abstract:In case that public safety has already caused extensive social concern in recent years, how to use surveillance video to detect abnormal pedestrians and prevent dangerous events becomes a hot topic. Abnormal pedestrians are those who are distinctly different from ordinary pedestrians in appearance, for example, using helmet to cover the face or ducking from the camera. Considering that the characteristics of abnormal pedestrians are mainly concentrated in head and face, this study proposes a fast detection method for abnormal pedestrians based on multi-task Convolutional Neural Network (CNN) and one-class Support Vector Machine (SVM) for head-facial features. First, we detect head-facial regions in surveillance video, then we use the multi-task CNN to extract features of these regions, and then we use one-class SVM to judge whether it is a normal pedestrian or not. In addition, this study designs a convolution kernel splitting method for CNN to accelerate the feature extraction speed. Finally, the experiment shows that the algorithm proposed in this study can effectively and quickly detect abnormal pedestrians in surveillance video.
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基金项目:国家重大科技专项(2017ZX03001019)
引用文本:
李俊杰,刘成林,朱明.基于多任务CNN的监控视频中异常行人快速检测.计算机系统应用,2018,27(11):78-83
LI Jun-Jie,LIU Cheng-Lin,ZHU Ming.Fast Abnormal Pedestrians Detection Based on Multi-Task CNN in Surveillance Video.COMPUTER SYSTEMS APPLICATIONS,2018,27(11):78-83

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