【作者】 高洪波；

【导师】 王洪玉；

【作者基本信息】 大连理工大学 ， 信号与信息处理， 2013， 博士

【摘要】 智能视频监控是根据摄像机采集的视频图像,对目标进行检测、跟踪,并对目标的行为进行识别和理解。目前,作为安防领域中最具发展前景的技术之一,视频监控系统得到了广泛的研究与应用,已经成为计算机视觉领域研究的热点。行人跟踪是视频监控系统的核心内容,然而,由于行人运动的多变、光照强度的变化和行人之间的相互遮挡等复杂情况的影响,跟踪器往往存在跟踪精度不高、目标丢失和身份切换等问题。本文以实现行人的鲁棒跟踪为主要研究目标,从多个关键技术入手,对行人跟踪算法进行系统的研究,包括行人检测算法、单摄像机行人跟踪算法和多摄像机行人跟踪算法等。本文的主要创新工作如下：(1)为在复杂场景中准确地检测行人,提出一种基于显著性的局部自相似描述符行人检测算法,该描述符在对数极坐标下表征行人的局部外形特征,并将其作为一个弱分类器,然后通过Adaboost算法将所有弱分类器合并成一个简单且有效的强分类器。该方法计算简单,较好地解决了行人之间的相互遮挡问题,提高了行人检测的准确率和精度。(2)研究了单摄像机跟踪算法。首先,针对跟踪器易于丢失目标的问题,提出了将投影直方图和质心偏移粒子滤波相结合的单摄像机单行人跟踪算法。该算法采用的投影颜色直方图引入了空间信息,相比于一般的颜色直方图具有更强的识别能力,同时用质心偏移代替粒子滤波中的重采样过程,提高了跟踪器处理目标被部分遮挡的能力。其次,为解决目标身份切换问题,提出了一种基于群组状态的单摄像机多行人跟踪算法,将多目标跟踪问题转化为最小化统一能量函数问题,该能量函数结合了条件随机场模型和标签代价函数。与传统方法相比,该方法减少了行人发生身份切换的概率,提高了跟踪算法的鲁棒性。(3)研究了多摄像机跟踪算法。首先,为了确定多摄像机之间的投影关系,提出了一种基于分层学习的二值描述符匹配算法,通过粗细两个层次的训练获得强分辨能力的二值描述符,提高了学习效率和匹配精度。其次,提出了一种基于加权一致的多摄像机多行人跟踪算法,该算法在网络流的框架下,结合目标的颜色特征和摄像机之间的投影关系,可增强算法处理目标间相互遮挡的能力,同时,利用颜色特征修正检测结果,克服了由检测器和单应性矩阵引起的误差,提高了跟踪算法精度。更多还原

【Abstract】 The purpose of video surveillance is to endow the computer vision system with human’s recognition ability to detect, track and comprehend the state of the object. As the core technology of security field, the video surveillance is getting popular in recent years. The pedestrian tracking is a base and core of video surveillance developed over decades, but detector has some blemish such as low precision, object lost and identify switch et al which due to the effect of the variety of pedestrian motion and illumination as well as the occlusion.The critical objective of the thesis is to research the robust pedestrian tracking algorithm in camera system. The contents of the thesis cover the tracking of single objcet single camera, multi-object single camera and multi-object multi-camera. Besides, the related methods are studied such as object detection and keypoint matching.The proposed algorithms are summarized as follows:(1) We present a high performance detector which included a discriminative LSSS descriptor based on the saliency. The descriptor represents the local shape feature of pedestrian in polar coordinates, and then Real AdaBoost algorithm is used to form a simple and effective strong classifier. This method is sample and improves the ability of dealing with the occlusion, which provides the perfect results for tracking-by-detection algorithm.(2) We study the single camera tracking algorithm. Firstly, to resolve the lost problem, a novel single object single camera tracking algorithm combining projection histograms with a centroid shift is proposed. The projection histograms are spatio-colorimetric presentation comparing with the classic color histograms, and the centroid shift is used instead of the resample process of the particle filter, it improves the ability of tracker to deal with part occlusion. Secondly, to decrease the phenomenon of label switch, the group based single camera multi-object tracking algorithm is proposed. The tracking problem is converted into minimum the energy function which combines the conditional random field mode and label cost. Experimental results demonstrate that the proposed algorithm decreases the probability of label switch and improves the robustness of the algorithm comparing with the classical methods.(3) The dissertation study the multi-camera tracking algorithm. Firstly, to obtain the projection relation between cameras, the approach presents a binary descriptor matching algorithm based on hierarchical learning method. The descriptor learning process is divided into two levels of coarse and fine, which combines the advantages of the fixed-point sampling mode and random sampling mode. It enhances the performance of learning. Secondly, the proposed work presents a multi-camera multi-object tracking algorithm based on weighted consensus, it combines the target’s color feature with the projection relation in network flow framework and enhances the power of dealing with occlusion. The weight consensus algorithm modifies the error caused by detector and camera calibration, therefore, it improves the tracking precision.更多还原