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\chapter{Summary}
\label{sec:struck_conclusions}

In this chapter, we have presented a new adaptive tracking-by-detection framework based on structured output prediction. Unlike existing methods based on classification, our algorithm does not rely on a heuristic intermediate step for producing labelled binary samples with which to update the classifier, which is often a source of error during tracking. Our approach uses an online structured output SVM learning framework, making it easy to incorporate image features and kernels. From a learning point of view, we take advantage of the well-studied large-margin theory of SVMs, which brings benefits in terms of generalisation and robustness to noise (both in the input and output spaces). To prevent unbounded growth in the number of support vectors, and allow real-time performance, we also introduced a budget maintenance mechanism for online structured output SVMs. We showed experimentally that our algorithm gives superior performance compared to state-of-the-art trackers.

 %which instead of relying on binary classifiers and treating tracking (\ie locating an object) as a combination of sliding window and object classification problem, uses structured output regression methodology. Our algorithm does not rely on any intermediate step for converting the detections to binary labelled examples for the update phase of the model, therefore, circumvents the need for heuristic labelling strategies which are prone errors. We formulated the tracking in online structured output SVM learning framework which is capable of easily using various kernels and features. In order to compensate the fact that in normal online learning scenarios with kernels, the number of support vectors grow unbounded and dominates the scalability of the algorithm, we also introduced a budget maintenance mechanism for online structured output SVM formulation. We showed experimentally, that our algorithm results in superior performance compared to the state-of-the-art trackers.

We believe that the structured output framework we presented provides a very rich platform for incorporating advanced concepts into tracking. For example, it would be relatively straightforward to extend the output space to include rotation and scale transformations. It would also be possible to incorporate object dynamics into this model. While these extensions focus on the output space, the input space could also be enriched through the use of alternative image features and multiple kernel learning.

%\noindent {\bf Acknowledgements} This work is supported by EPSRC CASE and KTP research grants and the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886. P.\ H.\ S.\ Torr is in receipt of Royal Society Wolfson Research Merit Award.

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