François Brémond
Research Director Inria Sophia Antipolis-Méditerranée Research Centre
François Brémond is a Research Director at Inria Sophia Antipolis Méditerranée, where he created the STARS team in 2012.
He has pioneered the combination of Artificial Intelligence, Machine Learning and Computer Vision for Video Understanding since 1993, both at Sophia-Antipolis and at USC (University of Southern California), LA. In 1997 he obtained his PhD degree in video understanding and pursued this work at USC on the interpretation of videos taken from UAV (Unmanned Airborne Vehicle).
In 2000, recruited as a researcher at Inria, he modeled human behavior for Scene Understanding: perception, multi-sensor fusion, spatio-temporal reasoning and activity recognition.
He is a co-founder of Keeneo, Ekinnox and Neosensys, three companies in intelligent video monitoring and business intelligence. He also co-founded the CoBTek team from Nice University in January 2012 with Prof. P. Robert from Nice Hospital on the study of behavioral disorders for older adults suffering from dementia.
He is author or co-author of more than 250 scientific papers published in international journals or conferences in video understanding.
He has (co)- supervised 20 PhD theses.
Lecture:
Action Recognition for People Monitoring
Abstract:
In this talk, we will discuss how Video Analytics can be applied to human monitoring using as input a video stream. Existing work has either focused on simple activities in real-life scenarios, or on the recognition of more complex (in terms of visual variabilities) activities in hand-clipped videos with well-defined temporal boundaries.
We still lack methods that can retrieve multiple instances of complex human activity in a continuous video (untrimmed) flow of data.
Therefore, we will first review few existing activity recognition/detection algorithms. Then, we will present several novel techniques for the recognition and detection of ADLs (Activities of Daily Living) from 2D video cameras.
We will illustrate the proposed activity monitoring approaches through several home care application datasets: Toyota SmartHome, NTU-RGB+D, Charades and Northwestern UCLA. We will end the talk by presenting some results on home care applications.
