摘要
近年来，随着社会安防意识的不断增强，视频监控系统得到了越来越多的重视。但是目前视频监控的智能化程度还不高，自动地进行人体行为分类及异常行为检测的技术还处于不断探索之中。本文在现有成果的基础上，对运动目标的分割，行为表征，特征提取，行为分类以及异常行为检测等方面进行了分析研究。
在运动目标检测上，总结了当前常用的几种方法，提出了背景减除与边缘提取相结合的目标检测算法。该算法主要是针对运动缓慢的人体目标及人体只有某些部位发生位移的情况，常规算法提取的人体轮廓是不完整的，本算法解决了这一问题。
在特征表征上，在原有的步态能量图基础上，提出了方差能量图及图像拆分算法。方差能量图比原始的步态能量图在识别率上明显提高；图像拆分在阴影及脚部遮挡等问题上都具有较好的鲁棒性。还提出了将“轮廓线到中心线的距离”作为信息特征进行行为分类，该算法比“轮廓线到中心点的距离”的识别率高出很多。
研究了线性流形学习的几种算法，提出了2D2MSDPCA和2D2MSD两种新的降维方法，它们不但克服了LDA的小样本问题，也增强了算法的行为分类能力及鲁棒性，最高识别率为100%。
在异常行为检测中，针对监控画面中运动目标离摄像头远近不同、在画面中所处位置不同以及画面倾斜等问题，提出了改进的Hu矩与改进的Hausdorff距离的有效融合的方法进行运动分析。将基于区域的离散Hu矩改为基于轮廓的离散Hu矩，并满足矩的三个不变性，该算法数据简洁，运算速度快；对Hausdorff距离进行了改进，将求最大值做为距离判断标准修改成为求均值，且去除方差最大的数据，该方法有效地剔除和平滑了噪声，使识别率明显提高；因为Hu矩提取全局不变量，全局噪声经常会淹没相似图像的细微差别，造成识别错误。因此，在Hu矩的基础上，将小波矩引入到异常行为检测中。提出了小波轮廓矩的概念，小波轮廓矩计算量少；对小波轮廓矩的参数m、n、q的取值进行了详细的分析，并对n参数的取值进行了改进。将小波轮廓矩与改进的Hausdorff距离融合，利用小波的时-频特性进行异常行为判断，异常行为识别率为97.92%。
In recent years, with growing awareness of social security, video surveillance system hasbeen more and more attention. However, the degree of intelligent video surveillance is nothigh, automatically human behavior classification and abnormal behavior detectiontechnology is still constantly being explored. On the basis of existing research results, weanalyze the moving target segmentation, behavioral characterization, feature extraction,categorization of actions, and abnormal behavior detection analysis.
In the aspect of moving target detection, summarized the current commonly methods, analgorithm of combination of background subtraction and edge detection is proposed. Whenthe slow movement of human and only certain parts of the body moving occurs, conventionalalgorithm can’t extract the complete body contour. Algorithm proposed in this paper cansolve the problem.
Based on gait energy image (GEI), variance of GEI (VGEI) and image segmentationalgorithm are proposed. VGEI gets higher recognition rate than GEI. Image segmentation candeal with the problems of blocked feet or shadows effectively, which has a better robustness.The feature of "distance of body contour to the centerline" is put forward, which obtainshigher recognition rate than “distance of the contour to the center point”.
In the aspect of linear manifold algorithms, both2D2MSDPCA and2D2MSD areproposed。They not only overcome the small sample size problem of LDA, but also improvethe capacity of behavior classification and enhance robustness, the highest recognition rate is100%.
In the detection of abnormal behavior, considering the different distance of the movingtarget to the camera, different location in the screen, inclined image and other issues,combination of improved Hu moments and improved Hausdorff distance method areproposed for motion analysis. Here, region-based discrete Hu moments are replaced by thecontour-based discrete Hu moments, which meet the three invariance of the moment andmake data of the algorithm simple. Improved Hausdorff distance is that the maximum as thedistance criterion is modified by the average as the distance criterion, and the largest varianceof the data is removed, the method effectively smoothes the noise, the recognition rate issignificantly increased. But Hu moments extract global variable, global noise often can drownsubtle differences between similar images, resulting in recognition errors. Wavelet moment isintroduced into the detection of abnormal behavior. We put forward the concept of waveletcontour moments. Computation of wavelet contour moments is fast; parameters-m, n, qvalues are analyzed in detail, and n parameter computation is improved. Using wavelet’s time-frequency characteristics to detect the abnormal behavior with improved Hausdorffdistance, abnormal behavior recognition rate reaches97.92%.
引文
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