Section: Scientific Foundations
The perception of motion is one of the major goals in computer vision with a wide range of promising applications. A prerequisite for motion analysis is motion modelling. Motion models span from rigid motion to complex articulated and/or deformable motion. Deformable objects form an interesting case because the models are closely related to the underlying physical phenomena. In the recent past, robust methods were developed for analysing rigid motion. This can be done either in image space or in 3D space. Image-space analysis is appealing and it requires sophisticated non-linear minimization methods and a probabilistic framework. An intrinsic difficulty with methods based on 2D data is the ambiguity of associating a multiple degree of freedom 3D model with image contours, texture and optical flow. Methods using 3D data are more relevant with respect to our recent research investigations. 3D data are produced using stereo or a multiple-camera setup. These data (surface patches, meshes, voxels, etc.) are matched against an articulated object model (based on cylindrical parts, implicit surfaces, conical parts, and so forth). The matching is carried out within a probabilistic framework (pair-wise registration, unsupervised learning, maximum likelihood with missing data).
Challenging problems are the detection and segmentation of multiple moving objects and of complex articulated objects, such as human-body motion, body-part motion, etc. It is crucial to be able to detect motion cues and to interpret them in terms of moving parts, independently of a prior model. Another difficult problem is to track articulated motion over time and to estimate the motions associated with each individual degree of freedom.