Treat the tracking problem as a classification task and use online learning techniques to update the object model

1 Based on structured output prediction (Structured SVM), in which the task is directly predict the change in object location between frames, instead of relying on a heuristic intermediate step for producting labelled binary samples with which to update the classifier, which is often a source of error during tracking

2 The online structured output SVM  learning framework is also easy to incorporate image features and kernels, and SVM also has good generalization ability,robustness to label noise and flexibility in object representation through the use of kernels

3 不是采用离目标近的为正样本，远的为负样本这种准侧，而是采用overlap，大于一定值的为正，小于为负

4 核函数

5 对支持向量进行限制，将权重影响小的去掉

 

1 the classification confidence function provides an accurate estimate of object position is not explicitly incorporated into the learning algorithm, since the classifier is trained only with binary labels and has no information about transformations

2 Examples used for training the classifier are all equally weighted, meaning that a negative example which overlaps significantly with the tracker bounding box is treated the same as one which overlaps very little. One implication of this is that slight inaccuracy during tracking can lead to poorly labelled examples, which are likely to reduce the accuracy of the classifier, in turn leading to further tracking inaccuracy

3  the labeller is usually chosen based on intuitions and heuristics, rather than having a tight coupling with the classifier. Mistakes made by the labeller manifest themselves as label noise, and many current state-of-the-art approaches try to overcome this problem by using robust loss functions [13, 14], semi-supervised learning [11, 17], or multiple-instance learning [3, 23]. We argue that all of these techniques, though justified in increasing the robustness of the classifier to label noise, are not addressing the real problem which stems from separating the labeller from the learner

1 总览

算法分为两步：1 预估物体的位移  2 更新判别函数

Structure Learning是一种允许输出为结构的学习方法，理论上任何输出都可以作为一种结构，即可以解决任意问题

Structure SVM 是结构学习的一种，已是一种比较成熟的算法实现框架，论文参考参考附件部分1,2,3 ，代码网址

2 建立 Structure SVM 模型

文中的模型为：

其中的约束条件是从>=0进化来的

1 >=0时w的解不唯一，所以我们选择是间隔最大的w并限制w的长度，——> >=1 

2 松弛>=1-

3 将1换为损失函数，越不同，间隔要求越严格（大）

3 解这个SVM模型

A

核心步骤基于SMO（序列最小最优化）Style的，SMO的基本思路是：选择两个变量（至少一个违反KKT），固定其他变量，进行两个变量的二次规划问题求解，这样将问题不断的分解为子问题进行求解，进而达到求解原问题的目标，SMO参考4,5

B

文中还引入了Budget来对支持向量的个数进行约束，进而可以达到实时方法为：

Similar to [21], we choose to remove the support vector which results in the smallest change to the weight vector w, as measured by ||?w||2

C 

Search over Y on a polar grid rather than considering every pixel offset.

1 采用的2个尺度的4X4的6种不同的haar-like（192 features）

2 Combine some different features by averaging multiple kernels

1 Large Margin Method for Structured Learning

2 Support Vector Machine Learning for Interdependent and Structured Output Spaces 1的简短版

3 Structured Learning and Prediction in Computer Vision

4 统计学习方法-李航 7.4

5 Sequential minimal optimization: A fast algorithm for training support vector machines

6 论文主页（含代码）