基于视频的运动目标检测与跟踪算法分析-计算机应用技术专业论文.docx

第 第 PAGE IV 页 第 第 III 页 出了一种将 Kalman 滤波器与 Mean Shift 相融合的算法，能比较稳定快 速地对运动目标进行跟踪，并有效地处理了遮挡问题。 关键词：目标检测，目标跟踪，混合高斯模型，LBP 纹理模型，D-S 证据 理论，Kalman 滤波，Mean Shift 跟踪 Video-based Moving Targets Detecting and Tracking Algorithm Research ABSTRACT Computer intelligent video surveillance system (CIVSS) is a front topic in Computer Vision domain. It spans many subjects including computer science，machine vision，image engineering，pattern analysis，artificial intelligence，etc. CIVSS can automatically analyze the sequence of images by the methods of computer vision and video analysis. The system can detect， locate，recognize and track objects in a moving environment in real-time. Furthermore，it can also analyze and judge the movement of objects. This thesis focuses on the background modeling and moving object detection，as well as moving object tracking. Background modeling is an important issue in accurate detection of moving objects. Existing work in the area has mostly addressed scenes that consist of static structures. In this paper, we present a novel non-parametric foreground-background model which explores the complex temporal and spatial dependencies in nonstationary scenes. The model adapts to scenes which contain small motions such as tree branches and water ripple, even shadow. The Model uses GMM(Gaussian Mixture Model) to compute the probability of foreground pixel with color information. It also uses LBP(Local Binary Pattern) texture model to compute the probability of foreground pixel with texture information. At last, it uses data fusion algorithm named D-S evidence theory to do a information fusion in the decision level. Extensive experiments with nonstationary scenes demonstrate the utility and performance of the proposed approach. At the aspect of object tracking, considering the drawbacks of the current Mean Shift tracking algorithm, we present a new algorithm combining the Kalman filter with Mean Shift. And the experiment results show that the moving objects