Recent years have been characterized by the overgrowth of video-surveillance systems and by automation of treatments they integrate. At the same time, object tracking has become, within years, a recurring problem in many domains and particularly in video-surveillance. In this dissertation, we propose a new object tracking method, based on the Ensemble Tracking method and integrating two main improvements. The first one lies on the separation of the heterogeneous feature space into a set of homogenous sub-spaces called modules and on the application, on each of them, of an Ensemble Tracking-based algorithm. The second one deals with the new tracking problem induced by this separation by building a specific particle filter. This filter weights each used module in order to estimate, for each frame in the sequence, both position and dimensions of the tracked object and the linear combination of modular decisions leading to the most discriminative observation. The results we present illustrate the global and individual efficiency of all the specific properties of our method and allow comparing this efficiency with the one of several reference tracking algorithms. Furthermore, all this work has led to an industrial development on the treatment systems of the partner company. In conclusion of this work, we present the prospects generated by these original developments, more particularly using the possibilities offered by the algorith mmodularity or making the modules choice dynamic according to their efficiency in a given situation.
