PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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132 Chapter 9. Visual Object Tracking (a) David (b) Girl (c) Face occlusion 2 (d) Sylvester Figure 9.13: Tracking performance depending on different values of α (blue) in comparison to a prior classifier trained at the first frame (black) and and on-line boosting tracking (red).
9.3. Experiments 133 Quantitative Comparison In Table 9.1 we depict detailed results for all tracking sequences compared to MILBoost [Babenko et al., 2009b], SemiBoost (OSB) and on-line AdaBoost (OAB) [Grabner and Bischof, 2006]. For OSB – as it is not based on robust loss functions – we used only the unsupervised loss by setting α = 1. For OSERB, we report the results with α = 0.5. Note that for tracking on-line DAS-RF and on-line MILForests are virtual the same; hence, we only report the results for MILForests. STB corresponds to boosting with space-time regularization. PSRF corresponds to on-line semi-supervised RFs using priors. ORF is the on-line random forest method introduced in Chapter 7 and RF is a random forest trained only at the first frame on the target object plus virtual samples. The latter is thus a static tracker similar to the one proposed in [Lepetit and Fua, 2006]. Sequence OSERB MILBoost OSB OAB ORF MILForest STB PSRF RF sylv 0.64 0.61 0.46 0.50 0.53 0.59 0.55 0.58 0.50 david 0.69 0.54 0.31 0.32 0.69 0.72 0.66 0.52 0.32 faceocc2 0.77 0.65 0.63 0.64 0.72 0.77 0.66 0.75 0.79 tiger1 0.65 0.51 0.17 0.27 0.38 0.55 0.43 0.59 0.34 tiger2 0.42 0.50 0.08 0.25 0.43 0.53 0.48 0.45 0.32 coke 0.2 0.33 0.08 0.25 0.35 0.35 0.38 0.17 0.15 faceocc1 0.77 0.63 0.71 0.47 0.71 0.77 0.65 0.75 0.77 girl 0.77 0.53 0.69 0.38 0.70 0.71 0.65 0.63 0.74 Table 9.1: Tracking results on the benchmark sequences measured as average detection window and ground truth overlap over 5 runs per sequence. The best performing method is marked bold-face and the second best underlined, respectively. As can be seen, for all of the sequences at least one of the proposed methods in this thesis is outperforming the state-of-the-art, i.e., MILBoost or AdaBoost. Additionally, it can be seen that OSERB always performs better than OSB; which speaks for the usage of robust loss functions and the convex combination of both a supervised and an unsupervised loss. The space-time regularized SERBoost (STB) also delivers accurate results; however, it cannot take pace with the SERBoost version that is only regularized with a prior classifier. We can also see that all random forest-based methods deliver accurate results. As expected, the self-learning variant ORF performs better on sequences that demand an adaptive classifier, such as tiger1 or coke. By contrast, the static random forest classifier RF performs best when it rather comes to occlusions and the adaptivity rate is not so important, as in faceocc1 and faceocc2. The prior regularized random forest version PSRF is somewhere between ORF and RF. On average, the best performing random forest
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PhD Thesis Semi-Supervised Ensemble
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Statutory Declaration I declare tha
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Most of all, I would like to thank
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learning. Finally, we hypothesize t
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sten Teil dieser Arbeit schlagen wi
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ii CONTENTS 3.6 Graph-based Methods
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iv CONTENTS 10 Conclusion 137 10.1
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vi LIST OF FIGURES 4.8 Performance
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viii LIST OF FIGURES 9.7 Comparison
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x LIST OF FIGURES
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xii LIST OF TABLES 8.2 Results and
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xiv LIST OF ALGORITHMS
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2 Chapter 1. Introduction Figure 1.
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4 Chapter 1. Introduction the liter
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6 Chapter 1. Introduction 1.1 Contr
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8 Chapter 1. Introduction
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10 Chapter 2. Preliminaries and Not
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26 Chapter 3. Overview of Semi-Supe
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Page 188 and 189: 162 BIBLIOGRAPHY [Xu et al., 2009]
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PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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