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AbstractLos sistemas de visión artificial juegan un impor-tante papel en numerosos Sistemas de T... more AbstractLos sistemas de visión artificial juegan un impor-tante papel en numerosos Sistemas de Transporte Inteligente (STI), tales como monitorización del tráfico, asistencia al conduc-tor o conducción automática de vehículos. Uno de los principales problemas que suelen ...
IET COMPUTER VISION, 2009
A method for robustly tracking and estimating the face pose of a person using stereo vision is pr... more A method for robustly tracking and estimating the face pose of a person using stereo vision is presented. The method is invariant to identity and does not require previous training. A face model is automatically initialised and constructed online: a fixed point distribution is superposed over the face when it is frontal to the cameras, and several appropriate points close to those locations are chosen for tracking. Using the stereo correspondence of the cameras, the three-dimensional (3D) coordinates of these points are extracted, and the 3D model is created. The 2D projections of the model points are tracked separately on the left and right images using SMAT. RANSAC and POSIT are used for 3D pose estimation. Head rotations up to +458 are correctly estimated. The approach runs in real time. The purpose of this method is to serve as the basis of a driver monitoring system, and has been tested on sequences recorded in a moving car.
uvigo.tv
AbstractLos sistemas de visión artificial juegan un impor-tante papel en numerosos Sistemas de T... more AbstractLos sistemas de visión artificial juegan un impor-tante papel en numerosos Sistemas de Transporte Inteligente (STI), tales como monitorización del tráfico, asistencia al conduc-tor o conducción automática de vehículos. Uno de los principales problemas que suelen ...
IET COMPUTER VISION, 2009
A method for robustly tracking and estimating the face pose of a person using stereo vision is pr... more A method for robustly tracking and estimating the face pose of a person using stereo vision is presented. The method is invariant to identity and does not require previous training. A face model is automatically initialised and constructed online: a fixed point distribution is superposed over the face when it is frontal to the cameras, and several appropriate points close to those locations are chosen for tracking. Using the stereo correspondence of the cameras, the three-dimensional (3D) coordinates of these points are extracted, and the 3D model is created. The 2D projections of the model points are tracked separately on the left and right images using SMAT. RANSAC and POSIT are used for 3D pose estimation. Head rotations up to +458 are correctly estimated. The approach runs in real time. The purpose of this method is to serve as the basis of a driver monitoring system, and has been tested on sequences recorded in a moving car.