We present an algorithm that can track multiple persons and their faces simultaneously in a video sequence, even if they are completely occluded from the camera's point of view. This algorithm is based on the detection and tracking of persons masks and their faces. Face localization uses skin detection based on color information with an adaptive thresholding. In order to handle occlusions, a Kalman filter is defined for each person that allows the prediction of the person bounding box, of the face bounding box and of its speed. In case of incomplete measurements (for instance, in case of partial occlusion), a partial Kalman filtering is done. Several results show the efficiency of this method. This algorithm allows real time processing.

International audienceWe propose to use both active contours and parametric models for lip contour extraction and tracking. In the first image, jumping snakes are used to detect outer and inner contour key points. These points initialize a lip parametric model composed of several cubic curves that are appropriate to the mouth deformations. According to a combined luminance and chrominance gradient, the initial model is optimized and precisely locked onto the lip contours. On subsequent images, the segmentation is based on the mouth bounding box and key point tracking. Quantitative and qualitative evaluations show the effectiveness of the algorithm for lip-reading applications

This paper describes a system for human body analysis (segmentation, tracking, face/hands localisation, posture recognition) from a single view that is fast and completely automatic. The system first extracts low-level data and uses part of the data for high-level interpretation. It can detect and track several persons even if they merge or are completely occluded by another person from the camera's point of view. For the high-level interpretation step, static posture recognition is performed using a belief theorybased classifier. The belief theory is considered here as a new approach for performing posture recognition and classification using imprecise and/or conflicting data. Four different static postures are considered: standing, sitting, squatting, and lying. The aim of this paper is to give a global view and an evaluation of the performances of the entire system and to describe in detail each of its processing steps, whereas our previous publications focused on a single part of the system. The efficiency and the limits of the system have been highlighted on a database of more than fifty video sequences where a dozen different individuals appear. This system allows real-time processing and aims at monitoring elderly people in video surveillance applications or at the mixing of real and virtual worlds in ambient intelligence systems.

The context of this work is to develop, adapt and integrate augmented reality related tools to enhance the emotion involved in cultural performances. Part of the work was dedicated to augmenting a stage in a live performance, with dance as an application case. In this paper, we present a milestone of this work, an augmented dance show that brings together several tools and technologies that were developed over the project's lifetime. This is the result of mixing an artistic process with scientific research and development. This augmented show brings to stage issues from the research fields of Human-Machine Interaction (HMI) and Augmented Reality (AR). Virtual elements are added on stage (visual and audio) and the dancer is able to interact with them in real-time, using different interaction techniques. The originality of this work is threefold. Firstly, we propose a set of movement-based interaction techniques that can be used independently on stage or in another context. In this set, some techniques are direct, while others go through a high level of abstraction. Namely, we performed movement-based emotion recognition on the dancer, and used the recognized emotions to generate emotional music pieces and emotional poses for a humanoid robot. Secondly, those interaction techniques rely on various interconnected systems that can be reassembled. We hence propose an integrated, interactive system for augmenting a live performance, a context where system failure is not tolerated. The final system can be adapted following the artist's preferences. Finally, those systems were validated through an on field experiment -the show itself -after which we gathered and analyzed the feedback from both the audience and the choreographer.

This paper presents a system that can automatically recognize four different static human body postures for video surveillance applications. The considered postures are standing, sitting, squatting, and lying. The data come from the persons 2D segmentation and from their face localization. It consists in distance measurements relative to a reference posture (standing, arms stretched horizontally). The recognition is based on data fusion using the belief theory, because this theory allows the modelling of imprecision and uncertainty. The efficiency and the limits of the recognition system are highlighted thanks to the processing of several thousands of frames. A considered application is the monitoring of elder people in hospitals or at home. This system allows real-time processing.