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Perception and Activity Understanding

The research group on the understanding of perception and activity conducts research analyses of human activities from multimodal data. This includes investigating the fundamental tasks of scene analysis such as detection, segmentation and tracking of people, their representation, and the characterization of their condition, as well as the modeling of sequential data and their interpretation in the form of gestures, activities, behavior, or social relationships, through the design of sound algorithms which exploit and extend models and methods of computer vision, machine learning, and multimodal data-fusion. Surveillance, traffic analysis, analysis of behavior, human-robot interfaces, and multimedia content analysis are the main application domains.

Current Group Members

Jean-Marc Odobez (EPFL Maître d'enseignment et de recherche)
Kenneth Alberto Funes Mora (Postdoctoral researcher)

Rui Hu (Postdoctoral researcher)
Gulcan Can (PhD student)
Nam D.Le (PhD student)
Weipeng He (PhD student)
Yu. Yu (PhD student)
Zoreh Moostani (Internship)



Cheng Chen
Wu Di
Stefan Duffner
Rémi Emonet
Alexandre Heili
Vasil Khalidov
Adolfo Lopez-Mendez
Xavier Naturel
Elisa Ricci
Carl Scheffler
Romain Tavenard
Jian Yao

Master Student:

Yiquiang Chen
Matthieu Duval
Navid Mahmoudian
Lukas Prestes

Doctoral students:

Sileye Ba
Mark Barnard
Datong Chen
Kenneth Alberto Funes Mora
Paul Gay
Alexandre Heili
Guillaume Lathoud
Stéphanie Lefèvre
Pedro Quelhas
Edgar Francisco Roman Rangel
Samira Sheikhi
Jagan Varadarajan

Current projects

MUMMER - MultiModal Mall Entertainment Robot
In MuMMER ("MultiModal Mall Entertainment Robot"), we propose to address the important and growing market of consumer entertainment robotics by advancing the technologies needed to support this area of robotics, and also by explicitly addressing issues of consumer acceptance, thus creating new European business and employment opportunities in consumer robotics.
VIEW-2 - Visibility Improvement for Events Webcasting
This project aims at developing innovative solutions to (1) improve the quality of multimedia presentation structuring and indexing by relying on several methodologies, like deep-neural networks for OCR slide processing, or active learning applied to ASR and OCR outputs to automatically generate semantic keywords; (2) using those indexes and keywords to improve the referencing of these presentation on the web.
LIFE - PdG-fatigue
L’objectif de ce projet est de réunir diverses compétences, matériels et savoir-faire autour de la caractérisation physiologique de la Fatigue chez les sportifs.
EUMSSI - EUMSSI - Event Understanding through Multimodal Social Stream Interpretation


Jean-Marc Odobez (EPFL Maître d'enseignment et de recherche)

Group News

Idiap has a new opening for a Postdoc position in deep learning and computer vision

Idiap has a new opening for a Postdoc position in deep learning and computer vision Dec 20, 2016

The Perception and Activity Understanding group has an opening in computer vision and machine learning.

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Idiap ranked first at the 2016 MediaEval Person Discovery challenge

Idiap ranked first at the 2016 MediaEval Person Discovery challenge Nov 15, 2016

The Eumssi team led by Idiap and comprising as well the LIUM partner ranked first out of 6 teams in the Person Discovery challenge of the MediaEval benchmarking initiative.

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