This paper focuses on dealing with the tracking challenges such as target occlusion and deformation. It proposes a new tracking method via extracting and evaluating multi-features for both target region and its adjacent surroundings. The multi-features separately describe the key factors to detect target including the color feature, the shape and contour feature, and the distributions of structure and intensity described by the Pearson Correlation Coefficient. These multi-features are proposed as the basic representation of target template and candidates and used to define a matching algorithm between them. The best matched candidate is taken as the final tracking result. To improve the efficiency of target template and candidates, the region of importance (ROI) for target is proposed by evaluating the distribution of salient values on many extended regions. The ROIs produce more accurate regions to form target template and candidates. Finally, a new template update method is defined based on the precision of tracked result to adapt to target state and achieve the follow target tracking. Using 25 videos in visual tracking benchmark, we achieve the quantitative and qualitatively evaluations of 12 different trackers. Many experiments demonstrate that our tracker produces much better results than the present trackers in dealing with target occlusion, deformation, rotation, background clutters.

本文着重处理跟踪挑战，例如目标遮挡和变形。通过提取和评估目标区域及其邻近环境的多特征，提出了一种新的跟踪方法。多种功能分别描述了检测目标的关键因素，包括颜色特征，形状和轮廓特征，以及由Pearson相关系数描述的结构和强度分布。这些多功能被提议为目标模板和候选对象的基本表示，并用于定义它们之间的匹配算法。最佳匹配的候选者将作为最终跟踪结果。为了提高目标模板和候选对象的效率，通过评估显着值在许多扩展区域上的分布，提出了目标的重要区域（ROI）。ROI产生更准确的区域以形成目标模板和候选对象。最后，基于跟踪结果的精度，定义了一种新的模板更新方法，以适应目标状态并实现跟踪目标跟踪。使用视觉跟踪基准测试中的25个视频，我们实现了对12种不同跟踪器的定量和定性评估。许多实验表明，在处理目标遮挡，变形，旋转，背景杂波方面，我们的跟踪器产生的效果比当前的跟踪器好得多。根据跟踪结果的精度定义了一种新的模板更新方法，以适应目标状态并实现目标跟踪。使用视觉跟踪基准测试中的25个视频，我们实现了对12种不同跟踪器的定量和定性评估。许多实验表明，在处理目标遮挡，变形，旋转，背景杂波方面，我们的跟踪器产生的效果比当前的跟踪器好得多。根据跟踪结果的精度，定义了一种新的模板更新方法，以适应目标状态并实现跟踪目标跟踪。使用视觉跟踪基准测试中的25个视频，我们实现了对12种不同跟踪器的定量和定性评估。许多实验表明，在处理目标遮挡，变形，旋转，背景杂波方面，我们的跟踪器产生的效果比当前的跟踪器好得多。