Human pose estimation using learnt probabilistic region similarities and partial configurations

A model of human appearance is presented for efficient pose estimation from real-world images. In common with related approaches, a high-level model defines a space of configurations which can be associated with image measurements and thus scored. A search is performed to identify good configuration(s). Such an approach is challenging because the configuration space is high dimensional, the search is global, and the appearance of humans in images is complex due to background clutter, shape uncertainty and texture. The system presented here is novel in several respects. The formulation allows differing numbers of parts to be parameterised and allows poses of differing dimensionality to be compared in a principled manner based upon learnt likelihood ratios. In contrast with current approaches, this allows a part based search in the presence of self occlusion. Furthermore, it provides a principled automatic approach to other object occlusion. View based probabilistic models of body part shapes are learnt that represent intra and inter person variability (in contrast to rigid geometric primitives). The probabilistic region for each part is transformed into the image using the configuration hypothesis and used to collect two appearance distributions for the part's foreground and adjacent background. Likelihood ratios for single parts are learnt from the dissimilarity of the foreground and adjacent background appearance distributions. It is important to note the distinction between this technique and restrictive foreground/background specific modelling. It is demonstrated that this likelihood allows better discrimination of body parts in real world images than contour to edge matching techniques. Furthermore, the likelihood is less sparse and noisy, making coarse sampling and local search more effective. A likelihood ratio for body part pairs with similar appearances is also learnt. Together with a model of inter-part distances this better describes correct higher dimensional configurations. Results from applying an optimization scheme to the likelihood model for challenging real world images are presented.