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视频监控中行人检测与分割算法研究
其他题名Study on Pedestrian Detection and Segmentation in Video Surveillance
秦瑞
学位类型工学硕士
导师李子青
2010-06-07
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
关键词行人检测 阴影去除 目标分割 局部纹理描述子 马尔可夫随机场 Pedestrian Detection Cast Shadow Removal Object Segmentation Local Texture Descriptor Markov Random Field
摘要随着“平安中国“等项目建设的逐步深入,智能视频监控系统以其独特的优势在安防领域得到了广泛的应用。行人是智能视频监控系统的重点关注对象。尽管在计算机视觉领域,目标检测与分割已经研究了十多年并取得了一定成就,但由于行人是非刚体目标,具有可变形性以及表象变化较复杂等特点,使得行人检测与分割依然是计算机视觉研究领域中难点之一。 本文主要针对视频监控场景中行人检测与分割问题展开深入研究,根据行人目标的特点提取兼具很强辨别能力且运算简单的目标特征,并构造对应的行人目标检测与分割快速算法,从而改进现有目标检测与分割算法在视频监控中的性能。论文主要工作如下: 1)针对滑动窗口遍历搜索冗余计算较多的问题,本文提出一种使用Adaboost与SVM混合组成的Cascade层级判别结构,结合Harr-like特征与HOG特征对于行人的不同编码描述能力,由粗到精进行快速滑动窗口检测判别。 2)针对视频监控场景中使用背景减除法分割运动行人目标的提取结果不完整的问题,本文提出一种基于背景模型获得前景目标相似度,对运动前景建立马尔可夫随机场模型,然后进行分割提取运动前景的方法。 3)针对视频监控场景中阴影对运动行人目标分割提取的影响,本文提出一种结合纹理与颜色混合信息对阴影进行建模的方法。该方法利用局部三元模式及亮度畸变描述子对阴影建模,对基于该阴影模型得到的相似度构成马尔可夫随机场,通过对随机场的最大后验概率求解分割去除运动前景中的阴影。为了使阴影模型能够适应场景的变化,本文采用在线学习的策略对阴影模型进行更新训练。 本文针对视频监控下的行人检测与分割进行了初步探索,为视频监控下行人检测与分割的应用方案提供了有益的理论算法基础。
其他摘要With the projects of " Security China", the intelligent surveillance system with its unique advantages, which can analyse the object' s behavior automatically, is becoming more and more necessary. Pedestrian is one of the most important objects in surveillance system. Detecting people in images is considered as one of the hardest tasks in object detection. The articulated structure and variable appearance of the human body, combined with illumination and pose variations, contribute to the complexity of the problem. Nevertheless pedestrian detection has been studied for decades, it has not been handled properly yet. This thesis targets the detection and segmentation of pedestrian in video surveillance, especially studies extracting suitable features for pedestrian detection with good ability of discriminative and low computational complexity, with employing the pedestrian detection and segmentation approach, to improve the performance of these approaches in intelligent surveillance system. The main contributions of this work are as follows: 1) The thesis proposes a cascade hierarchical rejection learning framework combining the SVM and the Adaboost discriminative model. By using this coarse to fine rejection structure, a fast and robust method for pedestrian detection has been evaluated by the experimental results. Combined with the motion information, the method can not only reduces the computational cost of sliding windows, but also reduces the false alarms in detection. 2)The thesis employs the Markov Random Field (MRF) to segment the moving foreground. In contrast to the segmentation using background subtraction, the method can improve the final result, which considers neighboring smooth information that will refine the final segmentation. Combined with the pedestrian detection approach, the silhouette of the pedestrian can be extracted properly. 3)The thesis proposes a method to handle the moving cast shadow in the background subtraction, based on a local texture descriptor called Scale Invariant Local Ternary Pattern (SILTP). The likelihood of cast shadows is derived using information in both color and texture. Finally, the posterior probability of cast shadow region is formulated by further incorporating prior contextual constrains using a Markov Random Field (MRF) model. The optimal solution is found via using graph cuts. An online learning scheme is introduced to shadow learning process in both texture and color space, which ...
馆藏号XWLW1546
其他标识符200728014628072
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7538
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
秦瑞. 视频监控中行人检测与分割算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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