【作者】 张莉；

【导师】 齐美彬； 蒋建国；

【作者基本信息】 合肥工业大学 ， 信号与信息处理， 2011， 硕士

【摘要】 随着计算机视觉的不断发展,单摄像机智能监控算法已日渐成熟,近年来,多摄像机网络中的目标跟踪逐渐被重视。由于被监控区域的广阔和摄像机视域有限之间的矛盾,以及对计算机和经济等方面的考虑,不可能用摄像机覆盖所有的被监控区域。因此,无重叠视域多摄像机监控环境下的目标跟踪就成为了广域视频监控研究的重要内容之一。由于在无重叠视域多摄像机视频监控系统中,目标在时间上和空间上都是分离的。如何对不同摄像机中的相同目标进行匹配是无重叠视域多摄像机目标跟踪的关键问题。为了解决这个问题,本文提出了一种结合多个目标表现模型和摄像机间拓扑关系的数据关联方法来实现目标跟踪。论文的主要工作和创新如下:(1)对无重叠视域多摄像机之间的目标表现模型及其匹配问题,提出了带有亮度转移函数的分片颜色直方图、UV色度模型和目标身高特征相结合的多表现模型及其匹配算法。其中,针对目标在多摄像机之间的颜色特征差异问题,首先使用亮度转移函数(BTF)来消除摄像机本身和环境光照导致的成像差异,并对目标进行分片处理,弥补了颜色直方图完全丢失空间信息的缺点,实验结果表明,使用带有亮度转移函数的分片颜色直方图提高了匹配的准确性。由于UV色度模型对于光照变化不敏感,因此本文使用UV色度模型进行目标匹配,在一定程度上降低了环境光照变化带来的匹配误差。而由于摄像机之间视角和焦距等不同,相同目标在不同摄像机下的成像身高会发生明显变化,但在一定的约束条件下,两个摄像机下的目标身高变化近似满足线性规律,因此本文利用身高转移模型排除了一些身高差异较大的候选目标。这三种匹配模型相互补充,大大提高了外观模型匹配的准确性。(2)针对无重叠视域多摄像机之间的拓扑关系获得速度慢这一缺点,本文提出了一种较为快速的在线拓扑关系获取方法。根据单摄像机目标跟踪的结果直接获得摄像机间的出入点,使用单高斯模型来描述单目标转移时间的概率分布,并用渐进累积方法获得摄像机间的转移时间概率分布,为目标匹配提供了一个可靠的时间线索。(3)考虑无重叠视域多摄像机系统的特性,提出利用D-S证据理论对目标的多表现模型和时空约束进行融合,实现对穿越不可见区域的目标的持续跟踪,避免证据之间的冲突,提高了目标跟踪的准确性。更多还原

【Abstract】 With the development of computer visual, single camera intelligent visual surveillance algorithms have been mature step by step, object tracking in multi-camera network gradually become the key issue in recent years. Considering the contradiction between the broadness of the surveillance areas and the limited view range of the signal camera, the computation amount and the economical efficiency, it is impossible for the cameras to cover the whole region to be watched. So object tracking, especially non-overlapping multi-camera object tracking become an important part of research in broad area video surveillance.In non-overlapping multi-camera video surveillance system, object is separated both in temporal and spatial. How to match the object from different cameras is the core issue of the non-overlapping multi-camera object tracking. In order to settle this problem, we propose a multi-feature data confusion algorithm which combines several object appearance features with the topology relationship among the cameras. The main job and contributions in this paper are list as follows:(1) As the matching problem of object appearance model in non-overlapping multi-camera system, multiply appearance features and their matching algorithm are proposed including fragment histogram corrected by bright transfer function, UV chrome model and highness of the objects. As to the problem of gray distortion of the object among multi-camera, we use brightness transfer function to reduce the imaging difference caused by the camera itself and the circumstance illumination, then, cut the object model into fragment to make up for the space information loss of the regular histogram. The result shows that the fragment histograms corrected by the brightness transfer function rise the accuracy of the matching. For the reason that the UV chrome model is obtuse to the change of circumstance illumination, a kind of UV chrome model is used to match the object. It loses the matching error come from the illumination change. Due to the difference of the visual angle and the focal distance of the cameras, the same object images formed in different cameras has an obvious discrepancy on object highness. But in the certain circumstances, the object highnesses from two cameras satisfy a certain linear discipline in a nearly way. So we use the highness transfer model to exclude some outsides. The three matching model complete each other mutually, and improve the accuracy of the appearance matching enormously. (2) Aimed at the drawback that learning the topology relationship of the multi-cameras network is very time consumed, so a rapid online topology obtain method is proposed in this paper. The exit and entry are detected according to the tracking result in single camera, and single Gaussian model is employed to describe the probability distribution of the translation time for a single object, and the cumulative method is used to maintain the probability distribution of the translation time between cameras, which gives a responsible time clue for the object matching.(3) Considering the character of non-overlapping multi-camera object tracking system, the D-S evidence theory is applied to merge the multi-appearance models and the time-space restriction. This algorithm has tracked the object through the invisible region successfully; it also has avoided the conflicts among the evidences and creased the precision of the tracking.更多还原