Time integration and statistical regulation applied to mobile objects detection in a sequence of images

This PhD thesis deals with the detection of moving objects in monocular image sequences. The first section presents the inherent problems of motion analysis in real applications. We propose a method robust to perturbations frequently encountered during acquisition of outdoor scenes. It appears three main directions for investigations, all of them pointing out the importance of the temporal axis, which is a specific dimension for motion analysis. In the first part, the image sequence is considered as a set of temporal signals. The temporal multi-scale decomposition enables the characterization of various dynamical behaviors of the objects being in the scene at a given instant. A second module integrates motion information. This elementary trajectography of moving objects provides a temporal prediction map, giving a confidence level of motion presence. Interactions between both sets of data are expressed within a statistical regularization. Markov random field models supply a formal framework to convey a priori knowledge of the primitives to be evaluated. A calibration method with qualitative boxes is presented to estimate model parameters. Our approach requires only simple computations and leads to a rather fast algorithm, that we evaluate in the last section over various typical sequences. (author)