Séminaire de Pr François Bremond, PULSAR, INRIA, Sophia Antipolis - Date : mardi 11 octobre 2011, 15h00 - Lieu : salle séminaire, Institut MICA, Institut Polytechnique de Hanoi

Intervenant :
Pr François Bremond, chercheur et directeur de l'équipe/projet PULSA de l'INRIA Sophia Antipolis

Date : mardi 11 octobre 2011, 15h00
Lieu : salle "seminar room", 9ème étage, Institut MICA, bâtiment B1, Hanoi University of Science and Technology
Interprète traducteur : le séminaire sera présenté en anglais

François Brémond is leading the PULSAR team at INRIA Sophia Antipolis. He designs and develops generic systems for dynamic scene interpretation. The targeted class of applications is the automatic interpretation of indoor and outdoor scenes observed with various sensors and in particular with static cameras. These systems detect and track mobile objects, which can be either humans or vehicles, and recognize their behaviours. He is particularly interested in filling the gap between sensor information (pixel level) and recognized activities (semantic level). In 1997 he obtained his PhD degree at INRIA in video understanding and François Brémond pursued his research work as a post doctorate at USC on the interpretation of videos taken from UAV (Unmanned Airborne Vehicle) in DARPA project VSAM (Visual Surveillance and Activity Monitoring). He also has participated to many European projects (PASSWORD, ADVISOR, AVITRACK, SERKET, CANTATA, COFRIEND), one DARPA project, several national projects (SAMSIT, SIC, VideoID …), seven industrial research contracts (RATP, FNCA, SNCF, ALSTOM, ST-MicroElectronics,…) and several international cooperations (USA, Taiwan, UK, Belgium) in video understanding. François Brémond is author or co-author of more than 100 scientific papers published in international journals or conferences in video understanding. In 2005 he was a co-fonder of Keeneo, a company in intelligent video surveillance. More information is available at: http://www-sop.inria.fr/members/Francois.Bremond/

Scene understanding is the process, often real time, of perceiving, analyzing and elaborating an interpretation of a 3D dynamic scene observed through a network of sensors (e.g. video cameras). This process consists mainly in matching signal information coming from sensors observing the scene with models which humans are using to understand the scene. Based on that, scene understanding is both adding and extracting semantic from the sensor data characterizing a scene. This scene can contain a number of physical objects of various types (e.g. people, vehicle) interacting with each others or with their environment (e.g. equipment) more or less structured. The scene can last few instants (e.g. the fall of a person) or few months (e.g. the depression of a person), can be limited to a laboratory slide observed through a microscope or go beyond the size of a city. Sensors include usually cameras (e.g. omni directional, infrared), but also may include microphones and other sensors (e.g. optical cells, contact sensors, physiological sensors, radars, smoke detectors).

Scene understanding is influenced by cognitive vision and it requires at least the melding of three areas: computer vision, cognition and software engineering. Scene understanding can achieve five levels of generic computer vision functionality of detection, localization, tracking, recognition and understanding. But scene understanding systems go beyond the detection of visual features such as corners, edges and moving regions to extract information related to the physical world which is meaningful for human operators. Its requirement is also to achieve more robust, resilient, adaptable computer vision functionalities by endowing them with a cognitive faculty: the ability to learn, adapt, weigh alternative solutions, and develop new strategies for analysis and interpretation.

In the first part of the talk, we will discuss how scene understanding can be applied to video surveillance. The second part of the talk presents an activity monitoring system which aims to analysis elderly behaviors.