Fast multi-view face tracking with pose estimation

In this paper, a fast and an effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier extracts faces of any pose from the background. Then more specific classifiers discriminate between different poses. The tree of classifiers is trained by hierarchically sub-sampling the pose space. Finally, Condensation algorithm is used for tracking the faces. Experiments show large improvements in terms of detection rate and processing speed compared to state-of-the-art algorithms

Model-based Behavioural Tracking and Scale Invariant Features in Omnidirectional Matching

Two classical but crucial and unsolved problems in Computer Vision are treated in this thesis: tracking and matching. The first part of the thesis deals with tracking, studying two of its main difficulties: object representation model drift and total occlusions. The second part considers the problem of point matching between omnidirectional images and between omnidirectional and planar images. Model drift is a major problem of tracking when the object representation model is updated on-line. In this thesis, we have developed a visual tracking algorithm that simultaneously tracks and builds a model of the tracked object. The model is computed using an incremental PCA algorithm that allows to weight samples. Thus, model drift is avoided by weighting samples added to the model according to a measure of confidence on the tracked patch. Furthermore, we have introduced also spatial weights for weighting pixels and increasing tracking accuracy in some regions of the tracked object. Total occlusions are another major problem in visual tracking. Indeed, a total occlusion hides completely the tracked object, making visual information unavailable for tracking. For handling this kind of situations, common in unconstrained scenarios, the Model cOrruption and Total Occlusion Handling (MOTOH) framework is introduced. In this framework, in addition to the model drift avoidance scheme described above, a total occlusion detection procedure is introduced. When a total occlusion is detected, the tracker switches to behavioural-based tracking, where instead of guiding the tracker with visual information, a behavioural model of motion is employed. Finally, a Scale Invariant Feature Transform (SIFT) for omnidirectional images is developed. The proposed algorithm generates two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image