Continous Head Pose Estimation using Random Regression Forests
نویسنده
چکیده
Head pose is a rich visual cue that finds great interest in the field of human robot interaction (HRI) and for video surveillance applications. Previous attempts at solving this problem have often proposed solutions formulated in a classification setting. Furthermore, strong assumptions on illumination and scale in an occlusion-free environment have usually been made. We propose a regression solution to head pose estimation based on random regression forests trained over SURF features. We believe that slight improvements would suffice to make it even more robust under unconstrained environments.
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