基于稀疏表示的在线视觉跟踪

目标跟踪是计算机视觉、模式识别领域一个非常热门而且极具挑战性的课题，其目的是能够克服遮挡、旋转、光照、突然运动、变形、复杂背景等因素的干扰，准确的确定目标的位置等参数。近来，稀疏表示理论在目标跟踪中的研究非常活跃，而且其理论在不断完善的过程中。本项目拟针对跟踪问题，围绕稀疏表示理论，研究能够处理目标旋转，姿态，光照，遮挡，以及复杂背景等鲁棒的跟踪算法。本课题的具体目标是探讨目标跟踪中的判决性稀疏字典学习与分类器的联合优化，同时学习出较好的字典和SVM分类器，从而实现生成模型和判决模型的有机融合；探讨基于局部模型的目标结构特征模型，从而将结构化信息与稀疏表示相结合；探讨各种可能的稀疏表示重构误差描述方法，建立一种基于学习的权重的重构误差描述方法，从而能更好的利用一些先验信息；最后探讨Lucas-kanade方法与稀疏表示目标函数联合优化，求解仿射变换参数，替代撒粒子的方法，加快跟踪的速度。

Object tracking is one popular and challenging topic in the field of computer vision and pattern recognition. The goal of object tracking is to locate the target accurately in the existence of occlusion, rotation, illumination change, abrupt motion, shape deformation, cluttered background, and other inferences. Recently, sparse representation theory becomes more and more active in the research of object tracking, and it is still a theory in process. The project intends to use sparse representation theory for the research of robust tracking method that can handle the difficulties of object rotation, pose variation, illumination change, occlusion and cluttered background. The objectives of the project are presented as follows: We explore the joint optimization of the discriminative dictionary and classifier in the object tracking problem. By learning a superior dictionary and a SVM classifier simultaneously, generative and discriminative models are incorporated organically. We also aim to combine structural information with sparse representation by constructing a structural local sparse appearance model of the target. Meanwhile, in order to make better use of prior information, we explore a variety of metrics measuring the reconstruction error in sparse representation and propose a learning based weighted distance metric. To speed up the tracking process, we plan to substitute the traditional particle filter by solving a collaborative model which incorporates Lucas-Kanade method and sparse representation to obtain affine parameters.