PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

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116 Chapter 9. Visual Object Tracking where t(x, y) is the image function of the template and f(x + u, y + v) is the sub-patch of the input image at location (u, v). ¯t and ¯f are the mean values of the template and the subpatch, σ t and σ f are the corresponding standard deviations, respectively. n is the number of pixels in the template. Note that for calculating the mean ¯f and standard deviations σ f one can use integral data structures in order to drastically speed-up the calculations of the pixel sums 1 . Template matching is not limited to simple raw pixel correspondences and can also be extended to higher-level representations, such as histograms of oriented gradients (HOGs) [Adam et al., 2006]. In order to model simple or complex motions in template tracking usually parametric models can also be incorporated [Shi and Tomasi, 1994, Baker and Matthews, 2004]. See also [Yilmaz et al., 2006] for a more detailed review. 9.1 Tracking as a discriminative Classification Problem A recently dominating trend in tracking is to apply appearance-based classifiers in order to track objects because they are able to deliver highly accurate results in real-time speeds. Such tracking-by-detection systems [Liu and Leordeanu, 2005, Avidan, 2007] usually train a classifier at the very first frame versus its local background and perform re-detection in the succeeding frames. In order to handle rapid appearance and illumination changes, recent works, e.g., [Grabner and Bischof, 2006] use on-line classifiers that perform self-updating on the target object. Such on-line classifiers are usually highly accurate and fast since they only have to discriminate the object from its current local background. Figure 9.1 illustrates the tracking approach as proposed in [Grabner and Bischof, 2006]. As can be seen, the process starts by marking the target object in frame t = 0. An initial classifier F 0 (x) is then trained based on X 0 = X 0 + ∪ X0 − , where X 0 + corresponds to one patch, the marked target object, and X0 − corresponds to surrounding negative patches. At frame t = t + 1 the trained classifier is applied in a sliding window manner in a predefined local search region in order to re-detect the marked object. The actual detection is positioned at the peak of the estimated confidence map. Then, similar to frame t = 0, F 0 (x) is on-line updated with X t+1 = X t+1 + ∪ Xt+1, − where X t+1 + corresponds to the estimated location of the target object, and Xt+1 − corresponds to surrounding patches of the estimated detection. Since this tracking process runs in loops, we also talk about a “tracking loop”. Although the approach of Grabner and Bischof has been shown to yield fast and highly accurate trackers, its main drawback lies in the fact that it can easily drift in case of wrong updates during learning [Matthews et al., 2004]. This comes from the fact that 1 www.idiom.com/ zilla/Papers/nvisionInterface/nip.html (14.5.2010)
9.1. Tracking as a discriminative Classification Problem 117 Figure 9.1: The original on-line AdaBoost tracking loop as proposed by [Grabner and Bischof, 2006]. Figure 9.2: Tracking of a textured patch with difficult background (same texture). As soon as the object becomes occluded the original tracker from [Grabner and Bischof, 2006] (dotted cyan), drifts away. Our proposed methods (yellow) successfully re-detects the object and continues tracking. the approach performs self-learning; i.e., the tracker relies only on its own predictions. Yet, during tracking it is hard to decide where to select the positive and negative updates necessary for self-updating. As we have seen above, usually simple heuristics are used where positive updates are taken at the peak of the confidence map and negative updates from low-confident regions. If the update patches are selected wrongly due to a wrong confidence map, errors can be accumulated over time ending up in learning wrong things. Additionally, if the object is in principle re-detected correctly but the alignment is not perfect also slightly wrong updates are generated (a.k.a. label jitter) and can lead to drifting. Figure 9.2 further illustrates the problem.
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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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62 Chapter 5. On-line Semi-Supervis
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64 Chapter 5. On-line Semi-Supervis
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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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