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

In this chapter, we have presented a novel approach to learning for real-time keypoint-based object detection and tracking. Our formulation generalises previous methods by combining the feature matching, learning, and object pose
estimation into a single structured learning framework. We showed how our framework allows an object model to be learned online, and presented an approximation to create an efficient way of using binary descriptors at runtime. During our experiments we observed that structured learning plays an important role in improving the detection rate compared to state-of-the-art static and learning-based feature matching techniques.

While we did not perform feature selection explicitly, our formulation implicitly is able to down-weight the less discriminative model features and therefore provides a good starting platform for further research into automatic online feature selection.

%\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.
