We present a novel similarity measure (likelihood) for estimating three-dimensional human pose from image silhouettes in model-based vision applications. One of the challenges in such approaches is the construction of a model-to-image likelihood that truly reflects the good configurations of the problem. This is hard, commonly due to the violation of consistency principle resulting in the introduction of spurious, unrelated peaks/minima that make the search for model localization difficult. We introduce an entirely continuous formulation which enforces model estimation consistency by means of an attraction/explanation silhouette-based term pair. We subsequently show how the proposed method provides significant consolidation and improved attraction zone around the desired likelihood configurations and elimination of some of the spurious ones. Finally, we present a skeleton-based smoothing method for the image silhouettes that stabilizes and accelerates the search process.
|Title of host publication||Proceedings of the 10th international conference in central Europe on computer graphics, visualization and computer vision (WSCG 2002),4-8 february 2002, Plzeň, Czech Republic|
|Publication status||Published - 2002|
|Name||Journal of WSCG|