一种用于异常行为检测的运动特征描述子

Motion Feature Descriptor for Abnormal Behavior Detection

目前,用于描述视频中人群的运动信息大多是基于光流的速度描述子。事实上,加速度蕴含丰富的运动信息,能够提供速度描述子在描述复杂运动模式时缺失的信息,以更好地表征复杂的运动模式。文中研究了一种运动特征描述子,使用受限玻尔兹曼机模型进行异常行为检测。首先,提取视频中的光流场信息,计算帧间加速度光流;然后,对一个时空块中的加速度信息进行直方图统计,将若干帧的所有时空块直方图特征进行拼接,从而获得加速度描述子;最后,在仅包含正常行为的训练集上建立受限玻尔兹曼机模型,在测试阶段根据测试视频重建特征与原始特征的误差大小进行异常检测。实验表明,所提出的加速度描述子结合速度描述子,在UMN数据集与UCF-Web数据集上,ROC曲线下的面积分别达到了0.984与0.958,相较于其他算法,所提方法取得了更高的异常行为检测准确率。

Modern motion description techniques for crowd motion in videos are mostly velocity descriptors based on optical flow.However,acceleration contains a wealth of motion information,which can provide information that the velocity descriptors are missing when describing complex motion patterns,and can better characterize complex motion patterns.This paper studies a motion descriptor,which uses an energy-based restricted Boltzmann machine model to perform anomalous behavior detection.Firstly,the optical flow information in the video is extracted,and the acceleration information is calculated through the optical flow information of two consecutive frames.Then,acceleration histogram feature is computed over spatial-temporal blocks,and all the spatial-temporal block histogram features of adjacent frames are spliced to obtain an acceleration descriptor.The Restricted Boltzmann Machine learns the normal motion patterns from the normal video training set,which is used for abnormal detection in terms of the errors of reconstructed data in detecting phase.The results show that the average area under the curve(AUC)of the UMN dataset reaches 0.984,and the area under the average curve(AUC)of UCF-Web reaches 0.958.Compared with other state-of-the-art algorithms,the proposed descriptor has superior performance on anomaly detection.