%0 Conference Proceedings
%F Teuliere10a
%A Teulière, C.
%A Marchand, E.
%A Eck, L.
%T Using multiple hypothesis in model-based tracking
%B IEEE Int. Conf. on Robotics and Automation, ICRA'10
%P 4559-4565
%C Anchorage, Alaska
%X Classic registration methods for model-based tracking try to align the projected edges of a 3D model with the edges of the image. However, wrong matches at low level can make these methods fail. This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Experiments on simulated and real video sequences show the improvement in robustness of the resulting tracker
%U http://rainbow-doc.irisa.fr/pdf/2010_icra_teuliere.pdf
%U http://doi.org/10.1109/ROBOT.2010.5509284
%8 May
%D 2010