Using multiple hypothesis in model-based tracking

— 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.

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Céline Teuliere, Éric Marchand, Laurent Eck
• Keywords: Classic Registration Methods, ICRA 2010, Multiple Low-level Hypothesis, Real Video Sequences, Robotics