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题名:
静态图像中人体检测技术研究
作者: 孙庆杰
答辩日期: 2004
专业: 计算机应用技术
授予单位: 中国科学院软件研究所
授予地点: 中国科学院软件研究所
学位: 博士
关键词: 人体检测 ; 概率模型 ; 纹理分析 ; 边缘检测 ; 矩形拟合 ; 肤色检测
其他题名: Study on Human Detection in a Static Image
摘要: 静态图像中的人体检测技术在驾驶员辅助系统、人体运动捕捉、色情图片过滤以及虚拟视频等领域有重要的应用价值。人体形状的变化比较复杂,而且人体可能穿着各种颜色和各种风格的衣服,因此检测静态图像中的人体是一个非常困难的任务。本文针对该问题开展了研究工作,并取得了以下几个创新成果:尽管人体形状的变化非常复杂,但人体四肢的变化相对比较小。基于此,本文在第二章提出一种强调四肢组合结构的人体模型,并利用样本学习的方法获得这种人体模型对应的概率模型,以便定量地反映人体结构的变化。图像中包含的边缘信息是物体检测系统中一种重要的特征。本文在第三章提出一种基于图像的亮度变化以及颜色空间分布的彩色边缘检测算法。该算法以灰度边缘检测为基础,补充颜色变化导致的边缘,并消除那些位于纹理区域内部的点。该算法检测到的边缘可以反映物体的轮廓信息,因此可以通过边缘组织高效地检测图像中的人体目标。本文在第四章提出一种基于矩形拟合的人体检测算法。该方法以第二章定义的人体模型为基础,依据知觉组织原则将图像中的边缘信息组织为高层特征。算法首先由边缘点得到线段,继而拟合矩形。根据矩形集合搜索可能的肢体,并根据肢体搜索可能的侧影。最后寻找合理的侧影组合,以便检测人体。该算法可以有效地处理人体形状的变化。本文在第五章提出一种算法检测人脸和双臂所在的图像区域。该方法首先在YCbCr颜色空间检测可能为肤色的象素,利用连通性分析得到皮肤区域。依据皮肤区域的最小包围矩形的一长宽比,确定可能的人脸候选。利用图像的边缘信息定义皮肤区域之间的连通距离,并根据连通距离检测每个人脸矩形可能对应的左臂和右臂,如果它们满足一定的几何和拓扑关系,则检测到一个人体目标。该方法充分利用了肤色属性,因此检测结果更加符合人的视觉系统对图像的感知。本文在第六章介绍了另外一个工作,即多视点绘制的多边形模板法。为了充分利用透视投影的共性,该算法在预处理阶段为场景中的每个多边形建立多边形模板,每个多边形模板由一条轮廓路径和一组纹条组成,而纹条是用平行成像面的面切割多边形所得的直线段。成像时一,对多边形以纹条为单位进行绘制。通过利用模板中包含的可复用的信息,该算法有效地提高了成像速度。本文利用人体的几何、拓扑以及肤色属性,研究了静态图像中的人体检测问题,并在Windows 2000操作系统下,利用C++实现了一个人体检测系统,同时给出了一些实验结果。进一步改善后,本文的人体检测算法可以应用在驾驶员辅助系统、运动捕捉、图片过滤和虚拟视频等领域。
英文摘要: The algorithm for human detection in a static image can be applied in many areas, such as driver assistance system, motion capture system, adult image filtering system and virtual video. However, as a human object may have various shapes and can be dressed in many different colors and styles, it is a challenging task to detect a human in a static image. In this paper, we outline some possible remedies in solving this problem, and the following contributions distinguish this dissertation from previous works: Although the shapes of humans can vary dramatically, the shape of limb is relatively simple. Based on the observation, we present a human model that emphasizes the shape of limbs, and we introduce a probability model for the human body to evaluate the variations of human shapes in chapter 2. Edge is an important image feature for object detection system, and we present a color edge detector based on the intensity variations and colors spatial distribution in chapter 3. Firstly, the algorithm detects gray edges and then further makes effort to find the color-texture edges. Finally, the detector removes the false edge pixels inside texture regions. The set of edge pixels detected by this algorithm is a useful cue for object's boundary, so it can be taken as an important feature for the human detection system. An algorithm of human detection is presented based on rectangle fitting in chapter 4. According to the principles of perceptual organization, the human detector takes edges as input, and then makes effort to obtain higher feature details step by step. The higher detail features include line segments, rectangles, limbs, OSBC (one-side body contour). Finally, we try to find the reasonable combinations among all the OSBCs to detect possible human objects. The algorithm can deal with the variations of human shapes effectively. An algorithm is presented to detect the combination of face and arms in chapter 5. The skin pixels are detected in the YCbCr space, and then the skin regions are determined according to the connectivity analysis. After that, face candidates are detected by the aspect ratio of the smallest bounding rectangles corresponding to the skin regions. The concept of connectivity distance is defined based on the edges of the original image. For each face candidate, its corresponding arms are detected in terms of the connectivity distance between the skin regions. If a combination of face and arms satisfies some geometric and topological constrains, it can be taken as a human object. As this algorithm makes use of skin colors, the detection results are more reliable. In chapter 6, we discuss another work that is an algorithm for rendering the same scene from multiple viewpoints, hi the pre-process, we define a splat for each polygon, and each splat is composed of a contour trace and a set of strips. After that, we can utilize the re-usable information contained in the splat to render each polygon. Compared with the traditional polygon scan-line algorithm, our new method can accelerate the rendering process effectively. hi this dissertation, we mainly study the algorithms for the human detection in a static image. A detection system is implemented in C++ under windows 2000 operating system, and some experiment results are presented. The algorithms may be applied in the driver assistance system, motion capture system, image filtering system and virtual video system in the future.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.iscas.ac.cn/handle/311060/5650
Appears in Collections:中科院软件所

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Recommended Citation:
孙庆杰. 静态图像中人体检测技术研究[D]. 中国科学院软件研究所. 中国科学院软件研究所. 2004-01-01.
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