A self-organizing map to improve vehicle detection in flow monitoring systems

The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-or...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2015-09, Vol.19 (9), p.2499-2509
Main Authors: Luque-Baena, R. M., López-Rubio, Ezequiel, Domínguez, E., Palomo, E. J., Jerez, J. M.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Automatic classification
Computational Intelligence
Control
Engineering
Errors
Focus
Mathematical Logic and Foundations
Mechatronics
Methods
Monitoring systems
Morphology
Neural networks
Remote sensing
Robotics
Segmentation
Self organizing maps
Surveillance
Surveillance systems
Vehicles
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Summary:The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distributions to detect unusual objects in the scene, and to improve the foreground mask handling occlusions between objects. After testing the proposed approach on several traffic sequences obtained from public repositories, the results demonstrate that this methodology is promising and suitable to correct segmentation errors on crowded scenes with rigid objects.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-014-1575-3