Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
Physical models for moving shadow and object detection in video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neighborhood-Based Competitive Network for Video Segmentation and Object Detection
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Shadow Detection and Removal from Solo Natural Image Based on Retinex Theory
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Loitering Detection Using Bayesian Appearance Tracker and List of Visitors
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Unsupervised Pedestrian Re-identification for Loitering Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Entropy Minimization for Shadow Removal
International Journal of Computer Vision
Efficient background subtraction and shadow removal for monochromatic video sequences
IEEE Transactions on Multimedia - Special section on communities and media computing
CCTV Video Analytics: Recent Advances and Limitations
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Shadows Removal by Edges Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Edge-Based Algorithm for Shadows and Ghosts Removing
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Qualitative robot localisation using information from cast shadows
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
A multi-layer scene model for video surveillance applications
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
An efficient and robust moving shadow removal algorithm and its applications in ITS
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
Shadow detecting using particle swarm optimization and the Kolmogorov test
Computers & Mathematics with Applications
Texture and space-time based moving objects segmentation and shadow removing
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Moving skin and shadow region analysis via adaptive models
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Reasoning about shadows in a mobile robot environment
Applied Intelligence
Mixture models with skin and shadow probabilities for fingertip input applications
Journal of Visual Communication and Image Representation
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Moving cast shadows are a major concern for foreground detection algorithms. The processing of foreground images in surveillance applications typically requires that such shadows be identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of nonuniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, including light saturated areas, and prevent false detection in regions where shadows cannot be detected. The proposed approach can be used with pixel-based descriptions of shadowed surfaces found in the literature. It significantly reduces their false detection rate without increasing the missed detection rate. Results obtained with different scene types and shadow models show the robustness of the approach.