Head pose estimation for driver monitoring

Head pose estimation is important for driver attention monitoring as well as for various human computer interaction tasks. In this paper, an adaptive head pose estimation method is proposed to overcome difficulties of existing approaches. The proposed method is based on the analysis of two approaches for head pose estimation from an image sequence, that is, principal component analysis (PCA) and 3D motion estimation. The algorithm performs accurate pose estimation by learning the subject appearance on-line. Depth information is used effectively in the algorithm to segment the head region even in a cluttered scene and to perform 3D head motion estimation based on optical flow constraints.

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