英语翻译In this paper,we substantialize the general adaptationproblem as a subspace adaptation problem in nonstationaryappearance tracking,where the target visual appearances fora short time interval are represented as a linear subspace.Weanalyze

英语翻译In this paper,we substantialize the general adaptationproblem as a subspace adaptation problem in nonstationaryappearance tracking,where the target visual appearances fora short time interval are represented as a linear subspace.Weanalyze
英语翻译
In this paper,we substantialize the general adaptation
problem as a subspace adaptation problem in nonstationary
appearance tracking,where the target visual appearances fora short time interval are represented as a linear subspace.We
analyze the ill-posed adaptive tracking problem in this setting.
In our approach,we enforce three novel constraints for the
optimal adaptation:1) negative data that are easily available,
2) pair-wise data constraints that are used to identify positive
data from bottom-up,and 3) adaptation dynamics that smooth
the updating process.Then,we give a closed-form solution to
this subspace tracking problem and also provide a practical
iterative algorithm.Our method not only estimates the motion
parameters of the target but also keeps track of the appearance
subspaces.Note our model adaptation strategy is fundamentally
different from the methods using on-line learning [23],
[25],where the appearance models at previous time frames are
directly utilized to supervise labelling the current image observations,
and to determine the new information for updating
the model.In contrast,we mainly resort to the data-driven
constraints to select positive and negative data from current
image observations for the appearance model adaptation.
In the next section,we briefly review related tracking algorithms
in terms of different observation models,afterwards the
dilemma in the traditional adaptation schemes is investigated.
Our solution to the dilemma of the adaptation is elaborated in
Section III.In Section IV,we present the experiments results
and discussions.The concluding remarks are given in Section V.
II.RELATED WORK AND MOTIVATION
In tracking,the target is tracked or detected based on the
matching between the observed visual evidence (or measurements)
and the visual model.We generally call the model that
stipulates the matching as the observation model or the measurement
model.Without any prior of the targets,appearancebased
tracking approaches are more general than feature-based
methods.The visual appearances of an object may bear a manifold
in the high-dimensional image space.Depending on the
features used to describe the target and on the variances of the
appearances,such a manifold can be quite nonlinear and complex.
Therefore,the complexity in the appearances largely determines
the degrees of difficulty of the tracking task.

英语翻译In this paper,we substantialize the general adaptationproblem as a subspace adaptation problem in nonstationaryappearance tracking,where the target visual appearances fora short time interval are represented as a linear subspace.Weanalyze
在本论文中,我们一般适应substantialize
问题作为一个非平稳子空间的适应问题
外观跟踪,其中目标视觉表象论坛短的时间间隔为一个线性子空间的代表.我们
分析病态在此设置的自适应跟踪问题.
在我们的方法,我们执行的三种新的限制
最佳改编：1）容易获得负面资料,
2）成对的数据,用于确定积极的约束
从自下而上的数据,以及3）适应的动力,顺利
然后,我们给出一个封闭形式解
这个子空间跟踪问题,并提供实际
迭代算法.我们的方法不只是估计的议案
目标参数,但也保留了外观轨道
子空间.请注意我们的模式适应战略是根本
不同于利用在线学习[23]方法,
[25],其中在以前的时间框架的外观模型
直接利用当前图像标签的监督意见,
并确定新的信息更新
该模型.与此相反,我们主要采取数据驱动
从目前的限制,选择正面和负面资料
为适应图像的外观模型观测.
在下一节中,我们简要地回顾相关跟踪算法
在不同的观察模式方面,事后的
在传统的适应计划的困境进行了研究.
我们的解决方案以适应困境是阐述
第三节.在第四节,我们目前的实验结果
和讨论.结束语给出了第五节
二.有关的工作和动机
在跟踪,目标是跟踪和检测的基础上
观察到的视觉匹配关系的证据（或测量）
和视觉模型.我们一般称之为模型
规定了作为观察或测量模型匹配
模型.没有任何事先的目标,appearancebased
跟踪方法更一般比基于特征
方法.对象的视觉外观可承受多方面
在高维图像空间.根据不同的
使用的功能描述和对目标的差异
外表,这样的流形可以很非线性和复杂.
因此,在出场的复杂性在很大程度上决定
跟踪任务的困难的程度.