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基于视频的行为识别关键技术研究
其他题名Research on Key Technologies of Video-based Behavior Recognition
徐东彬
学位类型工学博士
导师刘昌平 ; 黄磊
2009-05-27
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业计算机应用技术
关键词运动目标检测 静态目标检测 目标跟踪 行为识别 智能监控系统 Moving Object Detection Static Object Detection Object Tracking Behavior Recognition Intelligent Video Surveillance System
摘要当今社会存在着各种不安全、不和谐的因素,严重威胁到国家、社会和人们的安全。视频监控是在现有条件下,对涉及公共安全相关领域的场所进行实时监控,有效预防、消除安全隐患的主要技术手段之一。但是传统的视频监控系统存在众多不足,难以胜任复杂的监控场景和行为,需要研发基于行为识别技术的智能监控系统。本文对基于视频的行为识别的关键技术进行研究,包括:运动目标检测技术、静态目标检测技术、目标跟踪技术,应用这些技术可构建实用的行为识别系统用于安全防范和预警。本文的主要研究工作包括: 本文提出了一种核密度估计的自适应运动目标检测方法。通过对概率直方图进行分析,算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类。在此基础上,按照像素的概率来更新背景,并利用帧间差分背景模型和目标检测的结果,来解决背景更新中的死锁问题,同时解决背景突变的检测、更新问题。亮度、颜色和纹理信息被用于抑制运动目标的阴影干扰。 本文提出了一种融合运动特征和目标模型统计特征的静态目标(指目标大部分时间处于相对静止(或微小的运动)状态)检测方法。该方法克服了基于背景的运动目标检测方法难以应用到目标长时间静止的场合,同时克服了基于形状的目标检测算法对样本依赖、难以满足实时性的不足,创新地解决了静态目标的检测。该方法采用行列错位减图像的帧差来提取目标运动特征,基于目标模型和候选区域的统计特征匹配来检测目标,并根据目标运动特征和模板与候选区域的相似性度量动态更新模板。为了提高算法的实时性,本文还采用积分图优化特征提取。通过对摄像机抖动和强光干扰的抑制,提高了算法的性能。 本文还提出了一种基于改进的颜色相关图的粒子滤波算法用于目标跟踪。该方法采用改进的颜色相关图作为特征,包含目标的颜色和空间信息。用颜色相关图的上三角矩阵元素而不是整个颜色相关图构造目标特征,减少了内存消耗,降低了计算复杂度。该方法对目标分区域提取特征,融合了全局和局部特征信息,增强了特征区分能力。采用分区域更新模型的策略,避免了遮挡导致的模型更新错误,改善了跟踪性能。
其他摘要In the present world, unsafe and unharmonious factors seriously threaten the safeties of country, society and people. Video surveillance system is one of the main technical means available to effectively prevent and eliminate hidden danger in fields of public security by real-time monitoring. However, traditional video surveillance system has some shortcomings and is unable to cope with complex scenes and behaviors. So it is imperative to develop intelligent video surveillance system (IVSS) which is based on the techniques of behavior recognition. In this thesis, we focus on key techniques of behavior recognition based on video, including moving object detection, static object detection and object tracking. Based on analysis of the techniques, a preliminary behavior recognition system can be built. The main contributions of this thesis are summarized as: A method of kernel density estimation for adaptive motion detection (AKDE) is presented. To begin with, an approach for adaptive selecting thresholds of foreground and background was proposed by analyzing the probability histogram to classify pixels. Meanwhile, a background model updated according to probability is also provided. The background model of inter-frame difference incorporated with results of AKDE can solve deadlock problem in updating background model. It is also used to detect abruptly changed background and update background model. The gray, color and texture information are used to eliminate the disturbance of shadow, which improves performance of the algorithm. A method for static object detection is proposed by combining motion and statistical features. The static object here refers to the object in relatively static or in tiny movement. Compared with object detection method based on background subtraction, our method succeeds in overcoming the problem of detection objects when they are in long time static. Compared with object detection method based on shape, the proposed method is independent of samples and achieves real-time performance. The image of inter-frame difference, which is formed by one image subtracted form the image with a pixel offset in row and column, is used to obtain the motion feature. The statistic feature of the target region and candidate region is used to detect the target. The template of the target is updated according to motion feature and the similarity between target and candidate region. In order to improve the real-time performance of the algorithm, int...
馆藏号XWLW1382
其他标识符200618014629083
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6170
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
徐东彬. 基于视频的行为识别关键技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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