We present a model based approach to the integration of multiple cues for tracking high degree of freedom articulated motions and apply it to the problem of hand tracking using a single camera sequence. Hand tracking is particularly challenging because of occlusions, shading variations, and the high dimensionality of the motion. The novelty of our approach is in the combination of multiple sources of information which come from edges, optical flow and shading information. In particular, we introduce in deformable model theory a generalized version of the gradient-based optical flow constraint, that includes shading flow i.e., the variation of the shading of the object as it rotates with respect to the light source. This constraint unifies the shading and the optical flow constraints (it simplifies to each one of them, when the other is not present). Our use of cue information from the entirety of the hand enables us to track its complex articulated motion in the presence of shading changes. Given the model-based formulation, we use shading when the optical flow constraint is violated due to significant shading changes in a region. We use a forward recursive dynamic model to track the motion in response to 3D data derived forces applied to the model. The hand is modeled as a base link (palm) with five linked chains (fingers) while the allowable motion of the fingers is controlled by recursive dynamic constraints. Model driving forces are generated from edges, optical flow and shading. The effectiveness of our approach is demonstrated with experiments on a number of different hand motions with shading changes, rotations and occlusions of significant parts of the hand.
Model-based integration of visual cues for hand tracking
2002-01-01
442872 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Model-Based Integration of Visual Cues for Hand Tracking
British Library Conference Proceedings | 2002
|Using Multiple Cues for Hand Tracking and Model Refinement
British Library Conference Proceedings | 2003
|Tracking rigid objects using integration of model-based and model-free cues
British Library Online Contents | 2011
|Utilising Visual Attention Cues for Vehicle Detection and Tracking
British Library Conference Proceedings | 2020
|