A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation for robust tracking in the presence of severe occlusion
IEEE Transactions on Image Processing
A vehicle tracking system using PCA and adaptive resonance theory
SSIP'07 Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
A novel spatio-temporal approach to handle occlusions in vehicle tracking
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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Traffic monitoring systems based on image and sequence analyses are widely employed in Intelligent Transportation Systems (ITS's) in order to analyze traffic parameters and statistics. To this purpose, tracking objects is often needed. However, occlusions can mislead a vehicle tracking system based on a single camera, thus resulting in tracking errors. In this work we present a vehicle tracking algorithm based on the KLT feature tracker which exploits a Kohonen Self Organizing Map (SOM) to drastically reduce tracking errors arising from occlusions, thus increasing the overall robustness of the system. Our method has been implemented in a real-time traffic monitoring system that has been working on daily urban traffic scenes. The experimental results we present assess the effectiveness of our approach even in the presence of quite congestioned traffic situations.