A Probabilistic Framework for Joint Head Tracking and Pose Estimation
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Computer Vision and Image Understanding
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This work presents a novel approach for pedestrian head localization and head pose estimation in single images. The presented method addresses an environment of low resolution gray-value images taken from a moving camera with large variations in illumination and object appearance. The proposed algorithms are based on normalized detection confidence values of separate, pose associated classifiers. Those classifiers are trained using a modified one vs. all framework that tolerates outliers appearing in continuous head pose classes. Experiments on a large set of real world data show very good head localization and head pose estimation results even on the smallest considered head size of 7×7 pixels. These results can be obtained in a probabilistic form, which make them of a great value for pedestrian path prediction and risk assessment systems within video-based driver assistance systems or many other applications.