Video Understanding for Group Behavior Analysis
In the field of Cognitive Vision the Automatic Video Interpretation has become an important
research topic for real life applications, like Video Surveillance. The thesis goal is to recognize
the behaviors of a group of people (2-5 persons) involved in a scene depicted by a video
sequence. To solve this problem it is necessary to implement an automatic system to interpret
in real time the video.
So far, there are different research topics related to crowd or isolated individuals but only a
few works have addressed the recognition of group behavior.
The topic of this PhD thesis is the automatic recognition of behavior patterns in video
sequence for a group of people. We want to build a real time system able to recognize
different group scenarios.
This approach will include different tasks to achieve the final recognition. The first one
consists in tracking groups of moving regions detected in the video sequence acquired by the
cameras. The second task attempts to classify these moving regions into people classes.
Finally, the last task recognizes group scenarios using a priori knowledge containing scenario
models predefined by experts and also 3D geometric and semantic information of the
observed environment. A special emphasis will be put on the architecture, which being
general, must be easily adaptable to different domains.
The implementation of the approach will be investigated using SUP (Scene Understanding
Platform), which can track single objects (e.g. isolated individuals) and recognize simple
scenarios. A generic scenario description language was also designed to efficiently to
recognize predefined states and events [Van-Thinh Vu 2002]. This platform was developed in
the Pulsar research team at INRIA Sophia Antipolis. First, group scenarios will be defined
(and then recognized) using the general scenario description language. Second, the
likelihood of the group scenario recognition will be quantified. Third, machine learning
techniques will be investigated to learn and recognize these scenarios.
Time table:
Time period
6 months
Task
Introduction to the problem:
1. Study of existing solutions: group tracking and recognition of group behavior.
2. Study of similar problems: tracking and recognition of crowd or isolated
individual behavior.
3. Study of existing machine learning techniques.
6 months
Development of a generic approach for group tracking and recognition of group
behavior.
6 months
Evaluation of the proposed approach.
6 months
Improvement of the proposed approach.
Extension to estimate the likelihood of the group scenario recognition.
Machine learning technique investigation.
6 months
Validation of the proposed approach. Experiments with different scenes.
6 months
Concluding works; Preparation of a thesis.
Bibliography:
Van-Thinh Vu 2002: Van-Thinh Vu, Francois Bremond, and Monique Thonnat: Temporal
Constraints for Video Interpretation. Proceedings of the 15th European Conference on
Artificial Intelligence (ECAI'2002), Lyon, France, 21 - 26 July 2002; F. van Harmelen (Eds.),
IOS Press, 2002.
Required Background and Skills
Strong background in C++, cognitive vision, machine learning.
Location and Duration
3 years full-time within the PULSAR group of INRIA Sophia-Antipolis, FRANCE.
Supervisors
François BREMOND
Projet PULSAR, INRIA-Sophia Antipolis
2004 route des Lucioles BP 93
06902 Sophia Antipolis Cedex, FRANCE
Contacts
Email: francois.bremond@sophia.inria.fr
Tel: (+33) 4 92 38 76 59