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Author: 李育翔
Yu-Hsiang Lee
Thesis Title: Objects Tracking in Nonstationary Environment
Objects Tracking in Nonstationary Environment
Advisor: 鮑興國
Hsing-Kuo Pao
Committee: 李隆安
Lung-An Li
鍾國亮
Kuo-Liang Chung
李育杰
Yuh-Jye Lee
范欽雄
Chin-Shyurng Fahn
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2006
Graduation Academic Year: 94
Language: 英文
Pages: 48
Keywords (in Chinese): trackingGMMHMM
Keywords (in other languages): tracking, GMM, HMM
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We propose a method for tracking moving objects in a nonstationary environment.
While for stationary environment, background substraction has been widely used for object tracking,the same method cannot be applied when the background contains moving components, like watersurface, moving clouds or waving tree branches. Our method combines both of Gaussian Mixture Model and Hidden Markov Model to find the foreground objects. In the training process, Gaussian Mixture Model is used in the first place to obtain an approximated state estimation. Afterwards, the approximated values are fed into HMM as the initial seeds for EM iteration. By that, we hope to overcome the problem of local maxima, usually seen in the training of complicated Hidden Markov Model.
The method is done in a pixelwise manner, followed by a spatial smoothing technique which can resist noise. In the test part, the built pixelwise machines are adopted for real-time object tracking.


We propose a method for tracking moving objects in a nonstationary environment.
While for stationary environment, background substraction has been widely used for object tracking,the same method cannot be applied when the background contains moving components, like watersurface, moving clouds or waving tree branches. Our method combines both of Gaussian Mixture Model and Hidden Markov Model to find the foreground objects. In the training process, Gaussian Mixture Model is used in the first place to obtain an approximated state estimation. Afterwards, the approximated values are fed into HMM as the initial seeds for EM iteration. By that, we hope to overcome the problem of local maxima, usually seen in the training of complicated Hidden Markov Model.
The method is done in a pixelwise manner, followed by a spatial smoothing technique which can resist noise. In the test part, the built pixelwise machines are adopted for real-time object tracking.

Contents 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 ProposedMethod . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 State Estimation 5 2.1 GaussianMixtureModel . . . . . . . . . . . . . . . . . . . . . 6 2.2 HiddenMarkovModel . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Forward and Backward Algorithm . . . . . . . . . . . . 14 2.2.2 Baum-WelchMethod . . . . . . . . . . . . . . . . . . . 15 2.3 Model Selection Criteria . . . . . . . . . . . . . . . . . . . . . 17 2.4 SimpleModel to ComplexModel . . . . . . . . . . . . . . . . 18 3 Training and Prediction 21 3.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Dynamic Programming for Component Alignment . . . . . . . 22 3.3 Spatial Smoothing . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4 Experimental Results 28 4.1 Ex1: Watersurface . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2 Ex2: Grove . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5 Conclusions 33 5.1 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

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