视频监控中的行为序列分割与识别

智能监控系统中的行为分析与识别是当前计算机视觉领域的研究热点,而行为序列分割则是行为分析与识别的基础.提出了一种无监督的行为序列分割算法,并对分割结果进行识别.首先,采用鲁棒的形状编码方案得到人体轮廓的紧凑表示,提取轮廓点集特征描述运动人体;然后,基于奇异值分解(SVD)估计行为序列数据的本征维数,确定数据对应的低维流形,并通过检测特征数据在该流形上的投影误差的突变实现行为序列分割;最后,采用隐马尔可夫模型(HMM)对分割结果进行识别.在公共数据库上的实验结果表明了此分割和识别算法的有效性.

Time-sequential Activity Segmentation and Recognition for Video Surveillance

Human motion analysis in an intelligence surveillance system is a hot research topic in computer vision,and temporal segmentation of human activity sequence is the most fundamental step in human motion analysis.In this paper,an unsupervised online temporal segmentation algorithm is presented,and then the segmentation result is recognized by HMM.Firstly,a robust shape encoding scheme is employed to produce a compact representation of human silhouette,and a new feature called contour point set is proposed.Secondly,the intrinsic dimensionality of feature sequence and the corresponding low-dimensional manifolds are determined using SVD,and the break of projecting error of activity sequence on the determinate manifolds is detected as the segmentation point of the activity sequence.Temporal segmentation results are recognized by HMM finally.Experiments on two public databases show the effectiveness of the segmentation and recognition algorithms in this paper.