 |
    |
Unsupervised Activity Analysis and Monitoring Algorithms for Effective Surveillance Systems
Jean-Marc Odobez1, Cyril Carincotte2, Rémi Emonet1, Erwan Jouneau2, Sofia Zaidenberg3, Bertrand Ravera4, Francois Bremond3, and Andrea Grifoni5
1Idiap Research Institute, Switzerland
2Multitel, Belgium
3INRIA, France
4Thales Communication, France
5Thales Italia, Italy
Abstract. In this demonstration, we will show the different modules related to the automatic surveillance prototype developed in the context of the EU VANAHEIM project. Several components will be demonstrated on real data from the Torino metro. First, different unsupervised activity modeling algorithms that capture recurrent activities from long recordings will be illustrated. A contrario, they provide unusuallness measures that can be used to select the most interesting streams to be displayed in control rooms. Second, different scene analysis algorithms will be demonstrated, ranging from left-luggage detection to the automatic identification of groups and their tracking. Third, a set of situationnal reporting methods (flow and count monitoring in escalators and at platforms as well as human presence at lift ) that provide a global view of the activity in the metro station and are displayed on maps or along with analyzed video streams. Finally, an offline activity discovery tool based on long term recordings. All algorithms are integrated into a Video Management Solution using an innovative VideoWall module that will be demonstrated as well.
LNCS 7585, p. 675 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012