【作者】 乐应英；

【导师】 张学杰；

【作者基本信息】 云南大学 ， 计算机系统结构， 2010， 硕士

【摘要】 近年来,智能视频监控技术在各个领域得到了广泛的应用。运动目标检测和跟踪技术是智能视频监控系统的关键技术,一直是学术界研究的热点和难点问题。由于背景变化、光照条件、遮挡、阴影和目标物建模等的复杂性,目标检测和跟踪在理论研究和实践应用上都还面临着许多难题。本文在综合分析现有目标检测和跟踪算法的基础上,重点对动态背景下的目标检测和跟踪,以及多目标跟踪问题进行了研究,主要内容如下:(1)本文对智能视频监控中的相关关键技术进行了概述。比较和分析了常用目标检测算法:光流法、二帧差法、三帧差法、背景减除法和高斯建模法的原理和优缺点;研究了目标跟踪方法的分类、原理和典型算法:粒子滤波、Meanshift、Kalman Filter,并探讨了目标跟踪领域中的难点问题。(2)针对动态背景下的目标检测问题,本文提出了一种适用于动态背景下的基于背景运动参数估计的目标检测算法,并通过实验验证了算法的有效性。(3)本文重点研究了Camshift算法,在此基础上,针对Camshift在复杂动态背景下跟踪鲁棒性不高的缺点,提出了一种适用于动态背景下的改进Camshift目标跟踪算法。(4)为了克服传统Camshift只能跟踪单目标这一局限性,以及实现多目标跟踪中对运动、静止、再进入场景、恢复运动和退出场景目标的持续跟踪,论文提出了基于Camshift与目标轨迹跟踪相结合的多目标跟踪算法。更多还原

【Abstract】 In recent years, intelligent video surveillance technology has been widely used in various fields. Moving target detection and tracking is the key technology of intelligent video surveillance system, and has been the hot and difficult problems of academic research. Because of background changing, lighting conditions, occlusion, shadow, and the complexity of objects modeling in real scenes, target detection and tracking are still facing many problems both in theoretical study and practical applications.Based on the comprehensive analysis of existing target detection and tracking algorithm, this thesis extends the research mainly on two aspects:Target detection and tracking in dynamic background, and multi-target tracking problem. The main work of this thesis includes the following four aspects:(1) In this thesis, key technologies of intelligent video surveillance are summarized. The principle, advantages and disadvantages of currently used target detection algorithm (optical flow method, two-frame subtraction method, third-frame subtraction method, simple background subtraction method and the Gaussian Modeling method) are compared and analyzed; The classification and principles of currently used tracking methods are studied, the typical tracking algorithms: Particle Filter, Meanshift and Kalman Filter are explored, and current difficult problems of target tracking are summarized.(2) Aiming at the objects detecting problem in dynamic background, a detection algorithm based on the estimation of background motion parameters is presented to detect motion objects in dynamic background in this paper, and the validity of the algorithm is verified by experiment.(3) Aiming at the unsatisfied tracking robustness of Camshift in complex and dynamic background, an improved Camshift algorithm, which can be apply to dynamic background, is proposed.(4) In order to improve the demerit of Camshift that it only can be used in single object scenes, and to track the static object, out-scene objects, re-moving objects and re-enter objects in real applications, an improved algorithm based on the combination of motion trajectory and Camshift, is presented.更多还原