目标跟踪综合了图像处理、模式识别、机器学习、概率论和矩阵论等多个学科的研究内容，是目前计算机视觉里最热门的一个研究领域。随着计算机技术以及视频设备的不断发展，目标跟踪在智能视频监控、人机交互、交通监控以及军事军工等领域得到广泛应用。然而，目标跟踪仍面临着诸如光照亮度变化、目标形态变化、目标被遮挡、目标尺度变化和复杂背景等因素干扰。这使得设计准确性高、鲁棒性强和实时性好的目标跟踪算法仍然是一个研究热点和难点。 本文在收集、整理和分析国内外大量相关文献资料的基础上，对基于稀疏分析的单目标和多目标跟踪算法进行了重点研究，主要工作及创新点如下： (1)提出基于稀疏分析模型的抗亮度变化颜色聚类方法。由于环境光照亮度变化直接影响目标的颜色特征，因此，如何利用颜色特征将在不同亮度下的同一目标归并为一类是一个难题，这也是后续解决目标跟踪亮度变化因素干扰的关键所在。针对此问题，对不同亮度下的颜色特征进行研究，发现同一类颜色的RGB值分布在同一确定的超直线，通过稀疏成分方法对颜色亮度模型进行建模，提出了基于K超直线聚类(K-HyperLine Clustering，K-HLC)的抗亮度变化颜色聚类方法。经过实验证明，所提算法能够有效地消除亮度变化因素干扰，对比其他聚类算法具有更强的鲁棒性。 (2)提出基于超直线聚类的抗亮度变化彩色图像目标跟踪方法。颜色直方图是一种简单高效的特征描述子，基于颜色直方图的跟踪算法对目标形变鲁棒性强，但对亮度变化非常敏感。针对此问题，通过稀疏分析方法建立具有亮度不变特性的判别模型，提出基于K-HLC的彩色图像目标跟踪算法。该算法应用超直线聚类建立具有亮度不变特性的判别模型，可有效降低亮度变化对目标描述的影响，并提出基于候选框的尺度估计机制处理目标尺度变化问题，在跟踪过程中只对图像背景部分的超直线进行更新，从而提高跟踪速度。实验结果表明基于K-HLC的彩色图像目标跟踪算法具有较好的跟踪效果，特别是对光照亮度和目标形态发生剧烈变化的场景具有较强的鲁棒性。 (3)提出融合L1APG与稀疏直方图匹配的鲁棒目标跟踪算法。稀疏表示具有较高的重构精度，因此，广泛地应用于目标跟踪领域。基于加速近邻梯度法(Accelerated Proximal Gradient，APG)的L1APG稀疏表示跟踪算法虽然实时性高且对光照变化和部分遮挡鲁棒性强，但对物体的尺度变化以及完全遮挡的抗干扰能力弱。为了进一步增强L1APG的鲁棒性，提出将L1APG与稀疏直方图匹配相融合的鲁棒目标跟踪算法。通过利用样本与模板之间的稀疏直方图相似度对L1APG的样本观测似然值进行加权，进而更有效地利用目标的局部信息，同时提出一种自适应更新机制学习目标外观变化，从而比原L1APG算法更具有抗尺度变化与完全遮挡的能力。实验结果表明，所提算法不仅跟踪精度比L1APG提高90％，而且鲁棒性比其他基于稀疏表示的目标跟踪算法更强。 (4)提出基于K匹配追踪(K-Matching Pursuit，K-MP)的快速目标跟踪算法。结合判别模型和生成模型的稀疏协作模型(Sparse Collaborative Model，SCM)目标跟踪算法对各种干扰都具有较强的鲁棒性。但它在跟踪过程中需要进行大量高计算复杂度的稀疏分解计算，极大影响实时性。针对此问题，提出了一种基于K-MP的快速目标跟踪算法，利用l₂最小化方式从稀疏字典中选择K个基来分解信号，从而减少迭代过程，进而提高稀疏分解速度。通过理论证明和仿真实验验证K-MP能够无损地重构出原信号。不同的基准视频测试表明，所提目标跟踪算法不仅鲁棒性更强，跟踪速度也较SCM快近四倍。 (5)针对目标跟踪算法无法自动检测和再识别目标的问题，通过将实时目标检测算法融入到快速压缩感知目标跟踪(Fast Compressive Tracking，FCT)算法，设计了基于移动摄像机网络的实时多目标跟踪系统。通过基于跟踪置信度的调度机制调用检测算法处理目标被遮挡以及进入和移出摄像机视野的问题，利用跟踪算法以实时学习更新目标外观变化特征。针对检测目标大小不归一和背景干扰问题，提出自适应尺度随机采样算法来提取目标整体轮廓信息，从而有效地减少背景信息的干扰。为了使得网络中所有目标的标识符形成一致，通过分布式数据库保存并共享网络中目标的特征及其对应的标识符。经过各种实验证明，系统各个模块问能够协调运行，相对于其他多目标跟踪算法鲁棒性更强。 关键词：目标跟踪；颜色特征：稀疏表示；匹配追踪；超直线聚类；亮度变化；移动摄像机网络

 Object tracking, which embraces image processing, pattern recognition, machine learning, probability and matrix theory, is one of the popular research areas in computer vision. With the development of computer and video camera equipment technology, object tracking is widely applied in intelligent video surveillance, human computer interaction, traffic monitoring and military industry. However, it is still a challenging problem to develop a robust and real-time tracker for complex and dynamic scenes due to appearance changes caused by varying illumination, pose variation, occlusion, scale variation and complex background. In this dissertation, the research of sparse analysis based single object and multiple object tracking algorithm has been deeply done by collecting, collating and analyzing abundant domestic and foreign relevant papers and literatures. The major contributions of this dissertation are as follows: 1.An illumination invariant color classification basing on sparse analysis model is proposed. As the fact that color feature is directly affected by environment illumination, it is still a challenging problem to classify the color feature of same target under different illuminations, which is also a key issue to develop an illumination invariant tracker. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, the color classification is formulated as a hyperline clustering problem via a sparse component analysis model. Experiments demonstrate the outstanding illumination invariant performance and robustness of the proposed algorithm as compared to existing clustering algorithms. 2.A hyperline clustering baesed color object tracking robust to illumination change is presented. Color histogram is a simple yet effective statistic based feature description. Trackers relying on the strength of color histogram are robust to object deformation. However, they are sensitive to illumination changes. To overcome this limitation, a novel hyperline clustering based discriminant model, which is able to distinguish the object from its surrounding background, is proposed in this chapter. Furthermore, an anchor based scale estimation is presented to cope with shape deformation and scale variation. In order to speed up the tracker, only the hyperline of background is updated during tracking. Numerous experiments demonstrate that the proposed approach achieve favorable performance, especially in the illumination variant and shape deformation challenging situations. 3.A real-time robust object tracking via fusing L1APG and spares histogram is presented. Spare representation, which has a capacity of high reconstruction accuracy, is widely applied in object tracking. Thought spare representation based L1APG (Accelerated Proximal Gradient) tracker is running in real-time and robust to illumination variation and partial occlusion, it fails in some challenging situation, like scale changes and heavy occlusion. To strength the robust of L1APG, a real-time and robust object tracking algorithm fusing L1APG and spares histogram matching is proposed. The sparse histogram likelihood between sample and temple is calculated to weight the observation likelihood of L1APG sample, which takes local appearance information into consideration. And an adaptive update scheme is presented to learn the appearance changes of object. Thus, the proposed algorithm is more robust to scale changes and heavy occlusion than L1APG. Experiments on variant videos and qualitative and quantitative analyses show that the proposed algorithm not only increase more than 90% accuracy of LlAPG but also more robust to other spares representation based trackers. 4.A K-Matching Pursuit method for fast object tracking is proposed. Sparse Collaborative Model (SCM) tracker, which integrates discriminative model and generative model seamlessly, is robust to various challenges. However, it involves a lot of manipulations of sparse representation so that it is computationally expensive, which leads to low frame-rate. To solve this problem, a K-Matching Pursuit (K-MP) tracker is proposed to speed up the sparse representations in SCM. K-MP reconstructs a K-sparse signal by selecting K bases of a dictionary via l₂ -minimization. We mathematically demonstrate K-MP is able to successfully reconstruct a K-sparse signal. And, the tracking experiments verify the higher computational efficiency and tracking accuracy of the proposed K-MP tracker than that of the LASSO based SCM tracker. 5.In order to solve the problem that trackers fail to detect and re-identify object, a real-time multi-object tracking based on mobile camera network is proposed by integrating Fast Compressive Tracking (FCT) with a real-time detector. The detector handles the occlusions and entering/exiting of targets whilst tracker updates the appearance features of targets. An adaptive size random patch sampling is presented to alleviate the influence of background and to normalize the size of targets. In order to keep a consensus on the ID of the targets with the network, a distributed database where ID and appearance of targets are updated over time and shared among all cameras is introduced. Experiments on various benchmark dataset demonstrate the better performance of the proposed approach than other multi-object tracking algorithms. Keywords: Object tracking; color feature; sparse representation; matching pursuit; hyperling clustering; mobile camera network