Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Fast 2D Hand Tracking with Flocks of Features and Multi-Cue Integration
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Learning to Detect Moving Shadows in Dynamic Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical camera auto-calibration for traffic surveillance systems
Expert Systems with Applications: An International Journal
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This paper describes a target detection system on transport infrastructures, based on monocular vision. The goal is to detect and track vehicles and pedestrians, dealing with objects variability, different illumination conditions, shadows, occlusions and rotations. A background subtraction method, based on GMM and shadow detection algorithms are proposed to do the segmentation of the image. Finally a feature extraction, optical flow analysis and clustering methods are used for the tracking step. The algorithm requires no object model and prior knowledge and it is robust to illumination changes and shadows.