A novel framework is proposed under which robust matching and tracking of a 3D skeleton model of a human body from multiple views can be performed We propose a method for measuring the ambiguity of 2D measurements provided by each view. The ambiguity measurement is then used for selecting the best view for the most accurate match and tracking. A hybrid 2D-3D representation is chosen for modelling human body poses. The hybrid model is learnt using hierarchical principal component analysis. The CONDENSATION algorithm is used to robustly track and match 3D skeleton models in individual views.
Tracking hybrid 2D-3D human models from multiple views
1999-01-01
383783 byte
Conference paper
Electronic Resource
English
Tracking Hybrid 2D-3D Human Models from Multiple Views
British Library Conference Proceedings | 1999
|Markerless tracking of complex human motions from multiple views
British Library Online Contents | 2006
|Tracking across Multiple Cameras with Disjoint Views
British Library Conference Proceedings | 2003
|