Constant background hypothesis for background subtraction algorithms is often not applicable in real environments because of shadows, reflections, or small moving objects in the background: flickering screens in indoor scenes, or waving vegetation in outdoor ones. In both indoor and outdoor scenes, the use of color cues for background segmentation is limited by illumination variations when lights are switched or weather changes. This problem can be partially allievated using robust color coordinates or background update algorithms but an important part of the color information is lost by the former solution and the latter is often too specialized to cope with most of real environment constraints. This paper presents an approach using local kernel histograms and contour-based features. Local kernel histograms have the conventional histograms advantages avoiding their inherent drawbacks. Contour based features are more robust than color features regarding scene illumination variations. The proposed algorithm performances are emphasized in the experimental results using test scenes involving strong illumination variations and non static backgrounds.

This paper describes a method for articulated 3D upper body tracking in monocular scenes using a graphical model to represent an articulated body structure. Belief propagation on factor graphs is used to compute the marginal probabilities of limbs. The body model is a loose-limbed model including attraction factors between adjacent limbs and constraints to reject poses resulting in collisions. To solve ambiguities resulting from monocular view, robust contour and colour based cues are extracted from the images. Moreover, a set of constraints on the model articulations is implemented according to human pose capabilities. Quantitative and qualitative results illustrate the efficiency of the proposed algorithm.

No abstract

No abstract