A Framework for Background Detection in Video
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Segmentation of Planar Objects and Their Shadows in Motion Sequences
International Journal of Computer Vision
A Robust Video-Based Algorithm for Detecting Snow Movement in Traffic Scenes
Journal of Signal Processing Systems
Foreground and shadow detection for video surveillance
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Tricolor attenuation model for shadow detection
IEEE Transactions on Image Processing
Flexible background mixture models for foreground segmentation
Image and Vision Computing
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
MetroSurv: detecting events in subway stations
Multimedia Tools and Applications
Advances in background updating and shadow removing for motion detection algorithms
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Synthetic ground truth dataset to detect shadows cast by static objects in outdoors
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Cast shadow detection based on semi-supervised learning
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Background subtraction using running Gaussian average and frame difference
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
Background modeling methods for visual detection of maritime targets
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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Abstract: Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVOs) based on an object-level classification in MVOs, ghosts and shadows. Background suppression needs a background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVOs and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVOs segmentation and background update.