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基于主轴的人的跟踪
其他题名Principal Axis Based People Tracking
胡敏
学位类型工学硕士
导师胡卫明 ; 谭铁牛
2004-07-01
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
学位专业模式识别与智能系统
关键词人运动的视觉分析 人的跟踪 人的主轴 基于主轴的人的跟踪 基于主轴的多摄像机匹配 Visual Analysis Of Human Motion People Tracking Principal Axis Of Human Body Principal Axis Based People Tracking Principal
摘要人的跟踪是通过从连续图像帧间建立对应关系,实现对图像和图像序列中运动的人进行跟踪,给出其运动轨迹。它在智能监控、运动分析、人机交互以及虚拟现实等方面具有广泛的应用前景和潜在的经济价值。一直以来,人的跟踪受到国内外学术届和企业届的广泛关注,但作为计算机视觉领域中的一个研究热点和难点,仍然有很多理论与技术问题需待解决。一方面,动态场景中运动的快速分割、人体的非刚性运动、人体自遮挡和目标之间相互遮挡的处理一直是困扰人的跟踪的难题;另一方面,近些年来,随着大范围智能视觉监控系统的迫切需要,多摄像机的使用也给人的跟踪研究带来了一系列的挑战。本文以视觉监控为应用背景,对人的跟踪进行了深入的研究,提出了基于主轴的人的跟踪方法,并对此方法在单摄像机下人的跟踪、遮挡情况下人的跟踪以及多摄像机下人的跟踪等子课题上的应用进行了细致探讨和分析。大量的实验表明该方法的有效性和鲁棒性。本文的主要研究工作如下: ①基于人体关于主轴成对称分布的特点,提出了基于主轴人的跟踪方法,并将其应用到单摄像机下的跟踪中。首先,通过运动检测、运动目标分类提取对应于人的运动区域;然后给出三种情况下人的主轴自动提取方法;最后利用卡尔曼滤波器进行预测与跟踪。实验表明,同传统的基于特征的跟踪方法相比,人的主轴特征对噪声更加鲁棒,不依赖于准确的运动检测和分割。②基于运动目标的轮廓可以用投影直方图来表示,提出了一种基于垂直投影直方图的 运动目标分类方法。首先,对检测出来的运动区域在图像坐标系中对水平坐标轴进行投影得到运动区域的垂直投影直方图;然后,在垂直投影直方图的基础上定义了 离散度来作为分类度量标准,将运动物体分为两类:人和车辆。③提出了一种新的单摄像机中遮挡情况下人的跟踪方法。首先,引入贝叶斯网络,通过隐状态过程将遮挡关系模型融入跟踪中,从而将跟踪转化为概率传播中后验概率的求解问题;然后,利用基于主轴的椭圆形状模型和颜色模型作为人体模型的先验知识,在颜色模型和观测之间的Bhattacharyya距离的基础上定义了观测概率对观测进行评价;最后,利用粒子滤波算法进行求解。④提出了基于主轴的多摄像机匹配方法。该方法首先利用手工对应点恢复不同视角下图像平面之间的单映关系矩阵;然后基于单映关系的几何约束,定义了主轴匹配似然函数来衡量不同视角下主轴之间的匹配程度:最后给出了详细的多摄像机匹配算法步骤。
其他摘要As an important research topic of visual analysis of human motion, people tracking is to detect, localize and track moving people in video sequences captured by cameras. It has a wide spectrum of promising applications, such as visual surveillance, motion analysis, human computer interfaces, virtual analysis and so on. For a long time, it has received increasing attention from both academia and industry. However, in this field many theoretical and technical problems remain open. Firstly, fast motion segmentation, non-rigid human motion, self-occlusion of a person and occlusion between different moving objects are still vexing problems in people tracking. Secondly, as the need of visual surveillance in wide scenes, the use of multiple cameras in people tracking is challenged by a series of issues. The main application of people tracking is visual surveillance. In this thesis, we study people tracking in such an application, provide a novel method of principal axis based people tracking. This proposed method is applied to single view tracking, occlusion handling, and multi-view tracking. Experimental results show the effectiveness of the proposed method. The main work of the thesis is given below: ① Based on the constraint that human bodies are symmetric around principal axes, a novel principal axis based method is proposed for single view people tracking. For each frame image, the procedures of motion detection and object classification are to extract moving regions of people. Then, principal axes of people under different situations are automatically detected from moving regions. Kalman filtering is further applied to track people. ② Based on the idea that silhouettes of human bodies can be represented by projection histograms, a simple classifier is designed on the vertical projection histogram. In this thesis, we only consider two kinds of objects: human and vehicle, which are the most ordinary objects in lots of monitored scenes. During classification, vertical projection histograms are firstly created by projecting foreground pixels onto the horizontal axis of the image coordinate system. Based on vertical projection histograms, compactness of objects is then defined to classify the two categories. ③ A new method is provided to handling occlusion in single view people tracking. In this method, by introducing the Bayesian network during which the occlusion relation transition is represented by a hidden state process, the occlusion problem is modified as a posterior probability estimation during probability propagation. In observation measurement, 2D ellipse models and color histograms are used as prior knowledge. Then, observation likelihoods are defined on the Bhattacharyya distance between the models and observations. In tracking, the particle filtering algorithm is further applied
馆藏号XWLW787
其他标识符787
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6821
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
胡敏. 基于主轴的人的跟踪[D]. 中国科学院自动化研究所. 中国科学院研究生院,2004.
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