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In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foreground detection methods are discussed and a background modeling algorithm, which is based on multi-variate mixture of Gaussians, is proposed. During occlusion handling studies, a novel method for segmenting the occluded objects is proposed, in which a top-view of the scene, free of occlusions, is generated from multi-view data. The experiments indicate that the occlusion handling algorithm operates successfully on various test data. A novel tracking method by using multi-camera configurations is also proposed. The main idea of multi-camera employment is fusing the 2D information coming from the cameras to obtain a 3D information for better occlusion handling and seamless tracking. The proposed algorithm is tested on different data sets and it shows clear improvement over single camera tracker. Finally, multi-camera trajectories of objects are classified by proposed multi-camera event recognition method. In this method, concatenated different view trajectories are used to train Gaussian Mixture Hidden Markov Models. The experimental results indicate an improvement for the multi-camera event recognition performance over the event recognition by using single camera.

Cette thèse propose de nouvelles approches pour le calcul de cartes de profondeur associées à deux images stéréoscopiques.La difficulté du problème réside dans l'établissement de mises en correspondances entre les deux images stéréoscopiques. Cet établissement s'avère en effet incertain dans les zones de l'image qui sont homogènes, voire impossible en cas d'occultation.Afin de gérer ces deux problèmes, nos méthodes procèdent en deux étapes. Tout d'abord nous cherchons des mesures de profondeur fiables en comparant les deux images stéréoscopiques à l'aide de leurs segmentations associées. L'analyse des coûts de superpositions d'images, sur une base régionale et au travers d'échelles multiples, nous permet de réaliser des agrégations de coûts pertinentes, desquelles nous déduisons des mesures de disparités précises. De plus, cette analyse facilite la détection des zones de l'image de référence étant potentiellement occultées dans l’autre image de la paire stéréoscopique. Dans un deuxième temps, un mécanisme d'estimation se charge de trouver les profondeurs les plus plausibles, là où aucune mise en correspondance n'a pu être établie.L'ouvrage est scindé en deux parties : la première permettra au lecteur de se familiariser avec les problèmes fréquemment observés en analyse d'images stéréoscopiques. Il y trouvera également une brève introduction au traitement d'images morphologique. Dans une deuxième partie, nos opérateurs de calcul de profondeur sont présentés, détaillés et évalués. / In this thesis, we introduce new approaches dedicated to the computation of depth maps associated with a pair of stereo images.The main difficulty of this problem resides in the establishment of correspondences between the two stereoscopic images. Indeed, it is difficult to ascertain the relevance of matches occurring in homogeneous areas, whilst matches are infeasible for pixels occluded in one of the stereo views.In order to handle these two problems, our methods are composed of two steps. First, we search for reliable depth measures, by comparing the two images of the stereo pair with the help of their associated segmentations. The analysis of image superimposition costs, on a regional basis and across multiple scales, allows us to perform relevant cost aggregations, from which we deduce accurate disparity measures. Furthermore, this analysis facilitates the detection of the reference image areas, which are potentially occluded in the other image of the stereo pair. Second, an interpolation mechanism is devoted to the estimation of depth values, where no correspondence could have been established.The manuscript is divided into two parts: the first will allow the reader to become familiar with the problems and issues frequently encountered when analysing stereo images. A brief introduction to morphological image processing is also provided. In the second part, our algorithms to the computation of depth maps are introduced, detailed and evaluated.