Stabilizing Motion Tracking Using Retrieved Motion Priors (ICCV 2009)

In this video, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion capturing. The basic idea is to start human tracking without prior knowledge about the performed actions. The resulting 3D motion sequences, which may be corrupted due to tracking errors, are locally classified according to available motion categories. Depending on the classification result, a retrieval system supplies suitable motion priors, which are then used to regularize and stabilize the tracking in the next iteration step. Experiments with the HumanEVA-II benchmark show that tracking and classification are remarkably improved after few iterations.

Category:

Science & Technology

Tags:

• Meinard Müller
• Meinard Mueller
• Meinard Muller
• Andreas Baak
• ICCV
• motion templates
• computer vision
• computer science
• retrieval
• tracking
• classification
• annotation
• motion capture
• mocap
• HumanEVA