Driver's visual attention provides important clues about his/ her activities and awareness. To monitor driver's awareness, this paper proposes a real-time person-independent head tracking and pose estimation system using a monochromatic camera. The tracking and head-pose estimation tasks are formulated as regression problems. Three regression methods are proposed: (i) individual mapping on images for head tracking, (ii) direct mapping to subspace for head tracking, which predicts a subspace from one sample, and (iii) semantic piecewise regression for head-pose estimation. The approaches are evaluated on standard databases, and on several videos collected in vehicle environments. © 2012 Springer-Verlag.
CITATION STYLE
Zhang, Z., Kim, M., De La Torre, F., & Zhang, W. (2012). A real-time system for head tracking and pose estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6553 LNCS, pp. 329–341). https://doi.org/10.1007/978-3-642-35749-7_26
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