Survey on 2D and 3D human pose recovery

Human Pose Recovery approaches have been studied in the eld of Computer Vision for the last 40 years. Several approaches have been reported, and signi cant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we de ne a global taxonomy to group the model based methods and discuss their main advantages and drawbacks.Peer ReviewedPostprint (published version

Suivi 3D Monoculaire pour un Système de Vidéosurveillance à l'aide d'un Modèle de Mouvement et un Modèle d'Apparence

Session "Atelier VISAGES"ISBN : 978-2-9539515-2-3National audienceLe besoin en méthodes non intrusives d'analyse des mouvements humains se fait sentir à travers des applications comme la vidéosurveillance intelligente, les interfaces homme-machine et l'indexation multimédia. Dans cet article, nous proposons une approche générative se basant sur un filtre particulaire à recuit simulé (APF) : une fonction de vraisemblance qui combine des mesures basées sur les silhouettes et sur l'apparence, et un modèle temporel se basant sur une réduction de l'espace des poses pour une activité donnée. Le filtre proposé permet d'estimer en ligne la vitesse de marche ainsi que les coordonnées du cycle dans l'espace réduit. Nous évaluons l'approche proposée sur la base de données HumanEva. Les résultats du suivi montrent que la fonction de vraisemblance mixte réduit l'erreur 3D. Le modèle temporel proposée permet d'améliorer le suivi tout en réduisant le coût calculatoire du filtre particulaire

Mahalanobis Motion Generation

Representing motions as linear sums of principal components has become a widely accepted animation technique. While powerful, the simplest version of this approach is not particularly well suited to modeling the specific style of an individual whose motion had not yet been recorded when building the database: It would take an expert to adjust the PCA weights to obtain a motion style that is indistinguishable from his. Consequently, when realism is required, current practice is to perform a full motion capture session each time a new person must be considered. In this paper, we extend the PCA approach so that this requirement can be drastically reduced: For whole classes of motion such as walking or running, it is enough to observe the newcomer moving only once at a particular speed using either an optical motion capture system or a simple pair of synchronized video cameras. This one observation is used to compute a set of principal component weights that best approximates the motion and to extrapolate in real-time realistic animations of the same person walking or running at different speeds

Towards an Interactive Humanoid Companion with Visual Tracking Modalities

The idea of robots acting as human companions is not a particularly new or original one. Since the notion of “robot ” was created, the idea of robots replacing humans in dangerous, dirty and dull activities has been inseparably tied with the fantasy of human-like robots being friends and existing side by side with humans. In 1989, Engelberger (Engelberger

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used