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Tree-Structured Classifiers for Pose Estimation

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If you have a question about this talk, please contact Alan Blackwell.

Many computer vision tasks can be cast as large-scale classification problems, where extremely efficient and powerful classification methods are pursued. Boosting with decision stump learners, the state-of-the-art for objet detection, can be seen as a flat structure, while many developments including a Boosting cascade can be seen as a tree structure. Randomised Decision Forests is an emerging technique in the fields. A hierarchical structure yields many short paths, accelerating evaluation time, while feature randomisation promotes good generalisation to unseen data. It is inherently for multi-class classification problems. In this talk, we see applications of Randomised Decision Forests and tree-structured methods with comparisons and insights. The talk focuses on articulated hand pose estimation, and face recognition/landmarking. Hand and face are highly articulated and deformable objects, playing a key role for novel man-machine interfaces. Estimating their 3D postures, or regressing locations of joints/fiducial points is highly challenging. We have tackled the problems by various novel ideas on top of the cutting-edge techniques. We conclude the talk with some future directions including active interactive object recognition.

This talk is part of the Rainbow Group Seminars series.

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