Foundations and Trends® in Computer Graphics and Vision
An adaptation framework for head-pose classification in dynamic multi-view scenarios
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Head direction estimation from low resolution images with scene adaptation
Computer Vision and Image Understanding
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We present a method to estimate the coarse gaze directions of people from surveillance data. Unlike previous work we aim to do this without recourse to a large hand-labelled corpus of training data. In contrast we propose a method for learning a classifier without any hand labelled data using only the output from an automatic tracking system. A Conditional Random Field is used to model the interactions between the head motion, walking direction, and appearance to recover the gaze directions and simultaneously train randomised decision tree classifiers. Experiments demonstrate performance exceeding that of conventionally trained classifiers on two large surveillance datasets.