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)
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Occlusion Robust Vehicle Tracking based on SOM (Self-Organizing Map)
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A Simple Self-Calibration Method To Infer A Non-Parametric Model Of The Imaging System Noise
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Motion layer extraction in the presence of occlusion using graph cut
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In Intelligent Transportation Systems (ITS’s), the process of vehicle tracking is often needed to permit higher-level analysis. However, during occlusions features could “jump” from an object to another one, thus resulting in tracking errors. Our method exploits second order statistics to assess the correct membership of features to respective objects, thus reducing false alarms due to splitting. As a consequence, object’s properties like area and centroid can be extracted stemming from feature points with a higher precision. We firstly validated our method on toy sequences built ad hoc to produce strong occlusions artificially and subsequently on sequences taken from a traffic monitoring system. The experimental results we present prove the effectiveness of our approach even in the presence of strong occlusions. At present, the algorithm is working in the daytime in the Traffic Monitoring System of the city where we have been living.