CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleResearch on Classification Based Tracking Algorithm
Thesis Advisor唐明
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword目标跟踪 分类 随机性 Adaboost Mil Object Tracking Classification Random Adaboost Mil
Abstract目标跟踪是计算机视觉领域的主要研究方向之一,具有广泛的应用前景。处理目标跟踪问题有多种方法,基于分类模型的方法是其中的主流方法之一。所谓基于分类模型的目标跟踪方法,是指以目标和背景分别为正负样本,训练一个判别性的模型以区分二者,并进行在线更新。本文主要涉及boosting框架下的四种算法。传统的做法是,在每轮选择弱分类器时使损失函数尽量减小,以获得更小的训练误差。而我们的研究发现,对一部分弱分类器进行随机选取会使强分类器具有更好的跟踪性能。本文主要工作归纳如下: (1)回顾了四种基于分类的跟踪算法,包括多示例学习的跟踪方法(MIL)和另外三种基于boosting框架的方法,分别为AdaBoost,Gentle AdaBoost和SavageBoost。同时,针对MIL和SavageBoost算法存在的问题进行了改进。 (2)对不同的跟踪算法,在不同的视频序列上进行了大量的实验,发现在模型更新过程中随机选取一定比例的弱分类器可以改善算法的跟踪性能。实验结果表明,在实际应用过程中,具体的随机个数需视跟踪算法和待跟踪视频序列而定。
Other AbstractObject Tracking is one of the main research directions within the field of computer vision, which has significant applications. There are a variety of methods to solve tracking problem, a very important one of which is classification-based approach. That object tracking based on classification trains a discriminative model and updates online. In this thesis, we focus on four algorithms in the framework of boosting. In the exist way, weak classifier is picked that minimize loss function each round, and therefore reduce train error. However, with a large number of experiments, we found that randomly choosing a certain amount of weak classifiers improves tracking result.Our works can be concluded as follows: (1)Review four tracking algorithm based on boosting framework. They are online MIL, and three other algoritms based on boosting framework, online AdaBoost, online Gentle AdaBoost and online SavageBoost. Also, an improvement has been made on the problems in MIL and SavageBoost. (2)Through large number of experiment on different video clips of different tracking algorithm, we found that randomly choosing a certain proportion of weak classifiers can lead to more stable tracking. The experimental results show that in application, the best proportion of randomly chosen classifiers is subject to specific tracking algorithm and video sequence.
Other Identifier200928014628044
Document Type学位论文
Recommended Citation
GB/T 7714
李昕璨. 基于分类的跟踪算法比较研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
Files in This Item:
File Name/Size DocType Version Access License
CASIA_20092801462804(2607KB) 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[李昕璨]'s Articles
Baidu academic
Similar articles in Baidu academic
[李昕璨]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[李昕璨]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.