视频跟踪学习连接 Tracking Link

该视频跟踪系统：此链接尤为重要。它提供了丰富的资源,其中包含了多种跟踪算法的Matlab源码、数据集以及相关的性能测试结果

STRUCK追踪机制： 比较好的学习资源：<>

SCM跟踪算法
主页：http://faculty.ucmerced.edu/mhyang/project/cvpr12_scm.htm

Abstract
In this paper, we introduce a reliable object tracking algorithm by employing a collaborative model.

As the primary difficulty in object tracking lies in addressing the significant variation in visual appearance, we introduce a reliable appearance-based model that incorporates comprehensive template descriptions and localized feature extraction strategies.

We have developed two models based on sparsity: one discriminative (SDC) and one generative (SGM). Within the SDC module, we have devised an efficient approach for calculating confidence values, giving higher emphasis to the foreground compared to the background. The SGM module incorporates a unique histogram-based technique designed to consider both patch spatial information and handle occlusions effectively.

Furthermore, the update strategy takes into account both recent data points and a base template, thereby enabling the tracker to effectively address changes in visual appearance through precise updates while simultaneously reducing tracking drift by incorporating these factors.

A variety of experiments on complex video sequences were conducted to demonstrate that the proposed tracker achieves superior performance in competing with leading-edge methods.

一个比较好的学习连
接：<>

CMT跟踪算法：
官方主页 http://www.gnebehay.com/cmt/

更优的学习连接：
<>
其工作原理直接源自网络上的官方资料。如需进一步了解，请参考官方文档：

What is the underlying principle of this method?