【作者】 王聪；

【导师】 王海婴；

【作者基本信息】 北京邮电大学 ， 通信与信息系统， 2013， 硕士

【摘要】 在智能视频监控系统中,目标检测和跟踪的实质是通过对视频序列处理来提取运动目标,并得到目标在每帧视频中的位置、大小和运动速度等信息,起着承上启下的作用。其性能对智能视频监控系统的有效性会产生直接的影响,是智能视频监控系统极为重要的组成部分。本文首先介绍了Meanshift和Camshift算法的基本原理,给出了Camshift算法的搜索过程以及Meanshift和Camshift在视频目标跟踪中的应用。然后针对运动目标速度过快的情况,研究了Kalman滤波的相关知识。介绍了Kalman滤波器的基本原理与工作流程,并将Kalman滤波器应用到目标跟踪领域。在此基础上提出了与Camshift算法相结合的改进算法。对于背景颜色和目标相近或是背景较为复杂的场景,本文采取了前景提取的方法进行处理。这里利用Codebook背景建模的方法提取出运动目标前景,将此二值化图像作为mask模板来去除背景信息的各种干扰,获得所需要的前景信息。在此基础上为了完善跟踪效果,适应更多的应用场合,提出了利用改进的LBP局部二值化模式算法进行纹理信息的提取,与色调分量一起作为描述运动目标的特征,提高了目标跟踪的鲁棒性及适用范围。最后综合以上算法,提出了本文的最终算法,即融入了前景提取与运动估计的改进目标跟踪算法。实验证明,本文的算法在原有算法的基础上大大提高了跟踪性能。本文的主要创新点为结合Kalman滤波器解决运动目标速度过快问题,融入Codebook解决背景与前景区分不大的问题以及为提高跟踪精度加入纹理特征。更多还原

【Abstract】 In the intelligent video surveillance system, target detection and tracking based on video sequence is processed to extract the moving target, and obtain the target in each frame of the video in the location, size and velocity information. It plays an important role. The performance of target detection and tracking will have a direct impact on the validity of intelligent video surveillance system. It is an extremely important part of intelligent video surveillance system.This paper first describes the basic principle of the Meanshift and Camshift algorithm. The search process of Camshift algorithm is given as well as the application of the Meanshift and Camshift in video target tracking. Then Kalman filter is researched according to the condition of target moving so fast. The basic principle and work flow of the Kalman filter is introduced. Also Kalman filter is applied to target tracking. On this foundation this paper puts forward an improved algorithm combined with Camshift. Considering that the color of background is similar to the target or background is complex, this paper adopts the foreground extraction. Here Codebook background modeling method is used to extract the moving object. This binary image is used as a mask to remove the interference of background information and to gain the various prospects information needed. On this basis in order to improve the tracking performance and adapt to more applications, texture information is extracted by using improved local binary pattern algorithm. The tone component and texture information are both used as descriptions of the characteristics of moving target so as to improve the target track robustness and scope of application. Finally the final algorithm is presented in this paper based on the above algorithms, namely an improved target tracking algorithm combined with the foreground extraction and motion estimation. The experiments prove that the algorithm in this paper greatly improves the performance of tracking targets.The main innovations of this paper are combining Camshift with the Kalman filter to solve the problem of the target moving so fast, combining Codebook to distinguish background with foreground and adding texture feature to improve the precision of tracking.更多还原