A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Shadow and Object Detection in Traffic Scenes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Moving Shadow Detection with Support Vector Domain Description in the Color Ratios Space
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Introduction to Color Imaging Science
Introduction to Color Imaging Science
Shadow Flow: A Recursive Method to Learn Moving Cast Shadows
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Shadow detection for moving objects based on texture analysis
Pattern Recognition
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Measure and Shadow Casting Method for Object Tracking
ALPIT '07 Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)
A Robust Moving Object Detection Approach
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
Learning to Detect Moving Shadows in Dynamic Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous and Adaptive Learning of Shadows for Surveillance
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
A physical approach to Moving Cast Shadow Detection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Circuits and Systems for Video Technology
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Moving Cast Shadows Detection Using Ratio Edge
IEEE Transactions on Multimedia
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Physical models for moving shadow and object detection in video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Insignificant shadow detection for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Foreground and shadow segmentation based on a homography-correspondence pair
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Robust moving object detection against fast illumination change
Computer Vision and Image Understanding
Gait identification using shadow biometrics
Pattern Recognition Letters
A novel moving cast shadow detection of vehicles in traffic scene
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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We propose an adaptive shadow estimator to detect and eliminate the shadow of a moving object while adapting to variation of illumination and the environment in an automatic manner. The proposed method discriminates between the shadow and the moving object by cascading three estimators which use the properties of chromaticity, brightness, and local intensity ratio. In the spatial adjustment step, the method compensates for accumulated errors in the cascading process. Experimental results show that our scheme can operate in real-time, outperforms existing methods, and rapidly adapts to variations in the environment.