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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:
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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;
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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:
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Multi-people detection on a single time frame using a probabilistic occupancy map (POM)
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Multi-people tracking using dynamic programming
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Detection-by-classification from multiple views
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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
Contact
J. Berclaz [jerome.berclaz@epfl.ch],
F. Fleuret [francois.fleuret@idiap.ch]
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