摘要
在计算机视觉领域,人群异常行为检测技术可以广泛应用于视频监控、智能视频分析、群体行为识别等领域,因此,受到了学者们的广泛关注。由于视频中人群目标具有尺度变化大、透视形变、标注偏置等特点,人群异常行为检测依然是一个具有挑战性的难题。为此,本文提出了一种基于脉线流卷积神经网络的人群异常行为检测方法(streak flow CNN abnormal behavior detection,SFCNN–ABD)。SFCNN–ABD是一个双域网络,网络结构由两个深度残差网络作为骨干网络,分别为空域网络和时域网络。其中,空域网络的输入是原始视频帧,提取人群行为的表观特征;时域网络利用脉线流提取人群行为的运动特征,脉线流能更准确地识别场景中的空域和时域变化,因此能进一步提升人群异常行为检测的准确性。所提方法是通过卷积神经网络获取显著的人群行为空域特征,并通过脉线流结合卷积神经网络获取人群行为时域特征;最后,将两个网络的输出求取平均值,完成人群异常行为的检测。在UMN和VIF两个公开基准数据集进行了测试,实验结果表明本文方法的检测准确率优于当前主流算法,验证了本文方法的有效性。
Abnormal behavior detection in crowded scenes has drawn extensive interests in computer vision community due to its various applications,e.g.,video surveillance,video analytics,and action recognition.However,it is still a challenging task due to the large-scale variation,perspective distortion,and labeling biases.In this paper,an abnormal crowd behavior detection method based on streak flow and convolutional neural network(SFCNN–ABD)was proposed to address this problem.The SFCNN–ABD was composed of two stream CNNs,in which the backbone network was composed of two deep residual networks including spatial network and temporal network respectively.The spatial network utilized the raw video frames to extract the appearance features of crowded scenes,whereas the temporal network used the streak flow to extract the kinematic features.The streak flow can accurately recognize the spatial and temporal changes in the scene,and thus can further boost the proposed the performance.For abnormal behavior detection,salient spatial features were learned via a convolutional neural network(CNN)and temporal features with the aid of streak flow and CNN.Finally,the outputs of these two networks were averaged to detect the abnormal crowd behavior.Experimental results on UMN and VIF benchmark datasets indicate that the proposed method outperforms several state-of-the-art methods in terms of the accuracy and thus demonstrates the effectiveness of the proposed method.
作者
蒋俊
张卓君
高明亮
徐立宾
潘金凤
王新越
JIANG Jun;ZHANG Zhuojun;GAO Mingliang;XU Libin;PAN Jinfeng;WANG Xinyue(School of Computer Sci.,Southwest Petroleum Univ.,Chengdu 637001,China;School of Electrical&Electronic Eng.,Shandong Univ.of Technol.,Zibo 255000,China)
出处
《工程科学与技术》
EI
CAS
CSCD
北大核心
2020年第6期215-222,共8页
Advanced Engineering Sciences
基金
国家自然科学基金项目(61601266,61801272)。
关键词
人群异常行为检测
脉线流
时空卷积网络
残差网络
abnormal crowd behavior detection
streak flow
spatial–temporal CNN
ResNet101
