基于深度学习的视频异常行为识别算法

Video abnormal behavior identifying algorithm based on deep learning

针对传统的异常行为检测算法仅使用RGB图像作为网络的输入,而未考虑到视频序列中隐藏运动信息的问题,文中提出一种基于双流卷积神经网络的视频异常行为检测算法。该算法分别使用RGB图像与视频帧间的光流信息作为两个网络分支的输入来学习空间维信息与时间维信息,并使用长短时神经网络来建模长时视频帧间的依赖关系,从而得到最终的行为分类结果。仿真测试结果表明,所提出的方法在UCSD Ped1、Shanghai Tech和Pedestrian 2数据集上均能取得较好的识别效果,且使用帧间运动信息能够显著提升异常行为检测性能。

In allusion to the problem that,in the traditional abnormal behavior detection algorithms,only the RGB image is used as the input of the network,but the motion information hidden in video sequence is not considered,a video abnormal behavior detection algorithm based on two⁃stream convolutional neural network is proposed.In the algorithm,the optical flow information between RGB image and video frame is used as input of the two network branches to learn spatial dimension information and time dimensional information,and the long short⁃term neural network is used to build a model of the dependency relationship between long⁃term video frames,so as to get the final result of the behavior classification.The simulation testing results show that the proposed method can achieve better recognition results on the datasets of UCSD Ped1,Shanghai Tech and Pedestrian 2,and the use of inter⁃frame motion information can significantly improve the detection performance of abnormal behavior.