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%%%%%%%%%%%% TITLE
%%%\title{Struck: Structured Output Tracking with Kernels}
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%%%\author{Sam Hare$^1$ \quad \quad Amir Saffari$^{1,2}$ \quad \quad Philip H. S. Torr$^1$\\
%%%$^1$Oxford Brookes University, Oxford, UK \quad
%%%$^2$Sony Computer Entertainment Europe, London, UK\\
%%%%{\small\url{http://cms.brookes.ac.uk/research/visiongroup}} \quad \quad {\small\url{http://cms.brookes.ac.uk/vision}}
%%%{\small\tt \{sam.hare,philiptorr\}@brookes.ac.uk} \quad
%%%{\small\tt amir@ymer.org}
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%%%%%%%%%%%% ABSTRACT
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%%%%Adaptive tracking-by-detection methods are widely used for the task of tracking arbitrary objects. Current approaches treat the tracking problem as one of classification, and use online learning techniques in order to provide adaptivity. In order to update the classifier, it is necessary to use the current estimated object position to generate labelled examples, and it is not clear how best to perform this intermediate step. Furthermore, the goal of the learner is not explicitly coupled to that of the tracker. In this paper, we present an adaptive tracking framework based on structured output prediction. By explicitly allowing the output space to express the objectives of the tracker, we avoid the need for an intermediate step and its associated problems. Our approach uses an online learning algorithm based on a kernelized structured output support vector machine (SVM), providing great flexibility in terms of image features. For real-time applications, the unbounded growth of the number of support vectors learned during tracking would cause problems. To address this, we introduce a budgeting mechanism to keep the number of support vectors within a specified limit. Experimentally, we find that our approach outperforms state-of-the-art tracking-by-detections methods.
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%%%Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (accurate estimation of object position).
%%%In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we are able to avoid the need for an intermediate classification step. Our method uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow for real-time application, we introduce a budgeting mechanism which prevents the unbounded growth in the number of support vectors which would otherwise occur during tracking. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased performance.
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\part{Struck: Structured Output Tracking With Kernels}
\label{chp:struck}
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\input{chapters/struck/sections/intro}
\input{chapters/struck/sections/online-structured-output-svm}
\input{chapters/struck/sections/experiments}
\input{chapters/struck/sections/conclusions}

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