【作者】 胡斌斌；

【导师】 罗斌；

【作者基本信息】 安徽大学 ， 计算机应用技术， 2013， 硕士

【摘要】 随着计算机视觉的发展,智能视频监控得到了更广泛的发展。特别是随着人们对公共安全意识的提高,人们对人群场景中的异常行为检测越来越关注,对于异常检测的研究也越来越多。本文就智能视频监控中的人群异常检测展开工作,分析了几种常见人群异常行为,并相对的提出了一些异常检测方法。因为本文研究中高密度场景中的异常行为,因此首先要对场景中人群的密度进行分类,再对高密度的场景检测是否有异常事件的发生。对于人群密度估计问题,本文采用最小二乘法线性拟合和基于图像纹理分析的两种方法。线性拟合方法,利用前景检测方法,提取前景目标的像素数,人工统计场景中人群数目,利用最小二乘法线性拟合,此方法计算复杂度低,但该方法在人群遮挡严重时效果不理想。基于图像纹理的人群密度估计方法依靠不同密度的人群所体现的纹理信息不同,首先提取人群前景目标,然后计算前景目标的灰度共生矩阵,运用支持向量机方法进行密度的分类。该方法能解决人群出现遮挡的问题,而且由于只计算了前景目标的纹理信息,排除了背景的影响,因此分类结果的正确率得到了保障。针对传统基于跟踪的异常检测方法无法适用的拥挤人群场景,提出一种根据单元格速度和前景像素数(大小)及其运动方向是否具有刚性运动特性来判别异常的检测方法。为了只分析前景目标忽略不相关的背景,首先将输入帧进行前景分割,再将输入帧分割成不重叠的单元格,通过计算单元格中前景像素的光流提取每个单元格的运动特征来判定异常的发生。其中,速度特征可以检测出速度过快的异常情况。为了区分出车和因人群走近而形成的大目标,提出运动方向统计的方法。实验表明此方法在较短的时间内具有较好的检测效果。针对群殴和人群聚集事件,利用人群密度、运动强度、运动方向方差和人群面积变化等特征进行检测,对于人群聚集事件后的后续事件也有一定的预测效果。更多还原

【Abstract】 With the development of computer vision, intelligent video surveillance has been developed more widely. Especially with the improvement of people’s consciousness of public security, people have paid more and more attention to the abnormal behavior detection research in crowed scenes. In this thesis, we focus on the abnormal behavior detection of crowed scene in intelligent video surveillance system, analyze several common crowed abnormal behaviors, and put forward some relative anomaly detection method. As we study the abnormal behavior of high density scene, we should classify the scene crowed density firstly, then detect the abnormal events in the high density crowed scene.For the crowd density estimation problem, this thesis uses the method of least squares linear fit and the method based on image texture analysis. Linear fitting method, use foreground detection method to extract foreground pixels number of targets, count the number of population in the crowed scene artificially, use the method of least squares linear fit, this method has low computational complexity, but the method can’t be used in the crowd scenes where collision happens. Crowd density estimation method based on image texture information reflects the different density crowd, Firstly it extracts the crowd foreground object, and then calculates the foreground object of the gray level co-occurrence matrix, using support vector machine to classify density. This method can be used in the crowd scenes where collision happens. As this method just calculates foreground objects texture information, excludes the influence of the background, the correct rate of classification results is reliable.An efficient anomaly detection technique is proposed based on the cell speed as well as the number of foreground pixels and the movement direction. It is capable of dealing with crowded scenes where the traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into non-overlapping cells, followed by extracting motion features by computing the optical flow of the foreground pixels to discriminate the normal and the Anomaly. Speed characteristics can detect high-speed abnormalities. In order to distinguish between vehicle and large object formed by people walking close to each other, this thesis puts forward the statistical method of the direction of movement. Experiments demonstrate that the proposed method has better detecting effect in a short period of time. The fighting of a large number of people and people gathering event can be detected by characteristics such as crowed density, motion intensity, direction of motion and crowed area change. We also can predict the things after people gathering event happens.更多还原