The Evaluation and Extension of On-line Boosting for Object Tracking
Date Issued
2009
Date
2009
Author(s)
Chang, Tun-Chieh
Abstract
In this thesis, we present an extension of On-line Boosting on tracking problem which enhances the performance of the tracking system. We want to reduce the errors during the tracking process, and improve the accuracy and the robustness of the tracker. On-line Boosting is a discriminative-based training model, and it has the ability to fast distinguish the positive from the negative by analysing the distributions of both the target and background to build the decision boundary. This kind of learning model provides good adaptivity, and its ability is remarkable in the typical classification problems. But when the task becomes the challenging tracking problem, the variety of appearance changes of an object makes the model easily generating slight errors. The error would propagate through the tracking process and the accumulated errors cause tracking failure. For this reason, we want to combine the On-line Boosting with a more precise generative-based training model. Generative-based model can be viewed as a description of the object appearance, and it represents the subject in a more complex way. This kind of models usually play an important role of object recognition. Our tracking framework aims to take advantages of the slow but precise generative models, to compensate for the defects of On-line Boosting in tracking algorithm. Even more, we link two models by importance sampling and that retains the speed and the adaptivity of discriminative models. We evaluate our system by estimating the average position error in sequences, and we try our method on public testing data and the real-world data. The result shows that we can effectively combine two object training models with different concepts, making a balance collaboration between these methods, and finally improving the performance of the tracking system.
Subjects
Object tracking
On-line Boosting
Type
thesis
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