Using multiple hypothesis in model-based tracking

• Published in: 2010 IEEE International Conference on Robotics and Automation pp. 4559 - 4565
• Main Authors: Teulière, Céline, Marchand, Eric, Eck, Laurent
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2010
• Subjects: Cameras; Filtering; Image edge detection; Particle tracking; Predictive models; Robot vision systems; Robotics and automation; Robustness; USA Councils; Video sequences
• ISBN: 9781424450381; 1424450381
• ISSN: 1050-4729
• DOI: 10.1109/ROBOT.2010.5509284

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.