Incident Detection in a Multi-Camera Environment for Visual Surveillance Applications

To extend the capabilities of existing visual surveillance systems, we are developing a 3D framework dedicated to incident detection based on a multi-camera setup. Our goal in this project is two-fold:

1. Processing the output of several cameras in order to handle occlusions among people and their environment and provide us with more robust people detection and tracking strategies;
2. Capturing the motion of a person or a group of people in order to make the interpretation of abnormal behaviors much easier.

We aim at designing a system that combines the video flows from several cameras with overlapping views in order to generate a 3D representation of the scene under surveillance, potentially with the help of planimetric information when available.

Based on this representation, we will detect individuals and groups of individuals in the scene, to represent their relative positions in the 3D space and to analyze their behaviors and interactions.

Our project has been progressing so far through the following steps:

1. Multi-people detection on a single time frame using a probabilistic occupancy map (POM)


2. Multi-people tracking using dynamic programming


3. Detection-by-classification from multiple views


4. Anomaly detection using behavioral maps

Source Code

The source code that we wrote for the POM people detector part of this project has been released under a GPL license. You can download it from the Software page of our web site.

Data Sets

Some of the multi-camera video sequences that we acquired for this project are available for download on the Data part of our web site.

References

J. Berclaz, F. Fleuret and P. Fua, Multiple Object Tracking using Flow Linear Programming, 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (WInter-PETS 2009), Snowbird, Utah , December 2009.

J. Berclaz, A. Shahrokni, F. Fleuret, J. Ferryman and P. Fua, Evaluation of Probabilistic Occupancy Map People Detection for Surveillance Systems, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, June 2009.

J. Berclaz, F. Fleuret and P. Fua, Multi-Camera Tracking and Atypical Motion Detection with Behavioral Maps, European Conference on Computer Vision, October 2008.

J. Berclaz, F. Fleuret and P. Fua, Principled Detection-by-Classification from Multiple Views, Proceedings of the Third International Conference on Computer Vision Theory and Applications, Vol. 2, pp. 375 - 382, January 2008.

F. Fleuret, J. Berclaz, R. Lengagne and P. Fua, Multi-Camera People Tracking with a Probabilistic Occupancy Map, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, Nr. 2, pp. 267 - 282, February 2008.

J. Berclaz, F. Fleuret, and P. Fua, Robust People Tracking with Global Trajectory Optimization, Conference on Computer Vision and Pattern Recognition, 2006.

F. Fleuret, R. Lengagne and P. Fua, Fixed Point Probability Field for Complex Occlusion Handling, International Conference in Computer Vision, October 2005.

Contact

J. Berclaz [jerome.berclaz@epfl.ch],
F. Fleuret [francois.fleuret@idiap.ch]