【作者】 张琪；

【导师】 孔俊；

【作者基本信息】 东北师范大学 ， 计算机软件与理论， 2014， 硕士

【摘要】 近年来，随着数字图像处理技术的不断进步，智能视频监控系统越来越普及，它在建设智能城市、平安城市和智慧城市方面发挥着至关重要的作用。在计算机视觉领域，目标检测是智能视频监控系统中最核心的部分，是视频场景分析、处理和行为理解等视频内容分析的基础。然而，由于视频监控场景的复杂性，使得对于目标检测方法的研究仍然面临很多挑战。阴影是自然界中普遍存在的一种光学现象，具有与目标相似的两种视觉特性。一种是与目标类似，阴影与背景具有明显的差异性，另一种是阴影与目标具有相同的运动特性，这两种特性使得它很容易被误检测为目标。阴影的存在可能会造成目标粘连、目标形状扭曲，甚至目标丢失，致使目标检测的准确率降低，这将对后续的视频内容分析造成严重的影响。因此，阴影检测成为智能视频监控系统中的一个关键问题，具有重要的理论意义和广泛的应用价值。本文提出了一种应用于智能交通监控视频场景的阴影检测方法。该方法首先应用混合高斯模型对视频序列进行前景检测，在前景检测所得到的结果上进行后续的阴影检测。在初始检测阶段，首先应用训练样本对阴影和目标分别建立字典，进行阴影检测时，通过对比检测区域与两个字典的相似度判断该区域是否为阴影区域，同时，应用亮度特征进行阴影区域检测，然后融合基于重构误差的阴影检测结果及基于亮度变化的检测结果，提高了检测结果的准确性；在检测结果细化阶段，首先应用光照颜色不变性再对初始检测结果进行细化，最后使用后处理操作得到最终的检测结果。将本文提出的算法在四个数据库上进行了大量的仿真实验，并与经典的算法进行了对比。这四个数据库按摄像机镜头与路面距离由远及近分别命名为Video I，Video II，Video III和Video IV。实验结果表明，本文提出的算法能够准确地检测交通视频中的运动的阴影，并且优于对比的方法。更多还原

【Abstract】 In recent years, along with the advance of digital image processing techniques,intelligent video surveillance system becomes more and more popular, which plays a crucialrole in the construction of intelligent city, safe city and smart city. In the field of computervision, object detection is the basis of video content analysis including scene analysis,processing and behavior understanding. However, the research of object detection stillconfronts many challenges due to the complexity of video surveillance scenes.Shadow is one of the optical phenomena in nature as well as a kind of common dropphenomenon, which possesses two visual features similar to objects. One is that shadow andbackground have obvious differences, the other is that shadow and object have the samemotion characteristic. These two properties make it easy to be detected shadow as object. Theexisting of shadow reduces the accuracy of object detection, and may cause object merging,object shape distortion and even object loss. It will give rise to serious influence forsubsequent video content analysis. Therefore, shadow detection has become an inevitable keyproblem for intelligent video surveillance system, which has important theoreticalsignificance and extensive application value.This paper presents a hierarchical shadow detection method used in intelligent trafficsurveillance videos. First, the foreground is obtained by using Gaussian Mixture Model(GMM), and shadow detection is based on the result of foreground detection. Then, in therough detection stage, we construct shadow dictionary and object dictionary by using trainingsamples, the reconstruction error of foreground regions is adopted to determine whether theregion is shadow or not. Meanwhile, brightness feature is utilized to separate shadows fromforeground regions. The fusion of the two results improves the accuracy of shadow detection.In the refined detection stage, features derived from illumination invariant are applied torefine the rough detection result and the final detection result is obtained after spaceadjustment.The proposed method is simulation experimented on four databases, and compared withsome well-known methods. The four database are named as Video I, Video II, Video III andVideo IV，according to the distance between camera and the road. Experimental results showthat proposed method could detect the moving shadow accurately in traffic surveillance videoscenes, and the proposed method is better than the contrasts.更多还原