Re-identifying people in a network of cameras requires an invariant human representation.
State of the art algorithms are likely to fail in real-world scenarios due to serious perspective …

This paper presents an approach to detect and track groups of people in video-surveillance
applications, and to automatically recognize their behavior. This method keeps track of …

This paper addresses the problem of automatically acquiring context models from data.
Context and human behavior are represented using a state model, called situation model. This …

In this paper we propose an approach to recognize behaviors of groups of people in the
subway. Violent behavior or vandalism performed by a group can be detected in order to alert …

Ubiquitous environments may become a reality in a foreseeable future and research is aimed
on making them more and more adapted and comfortable for users. Our work consists on …

This paper makes use of recent advances in group tracking and behavior recognition to
process large amounts of video surveillance data from an underground railway station and …

New technologies bring a multiplicity of new possibilities for users to work with computers.
Not only are spaces more and more equipped with stationary computers or notebooks, but …

This paper presents a novel and unsupervised approach for discovering “sudden” movements
in video surveillance videos. The proposed approach automatically detects quick motions …

This paper addresses the problem of automatic learning of scenarios. A ubiquitous computing
environment must have the ability to perceive its occupants and their activities in order to …

Cette thèse étudie l'acquisition automatique par apprentissage d'un modèle de contexte
pour un utilisateur dans un environnement ubiquitaire. Dans un tel environnement, les …