Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient hierarchical method for background subtraction
Pattern Recognition
Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis
ACIVS '09 Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems
Spatial-temporal nonparametric background subtraction in dynamic scenes
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Fusing color and texture features for background model
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A kalman filter based background updating algorithm robust to sharp illumination changes
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Local spatial co-occurrence for background subtraction via adaptive binned kernel estimation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Statistical Background Subtraction Using Spatial Cues
IEEE Transactions on Circuits and Systems for Video Technology
Journal of Visual Communication and Image Representation
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Moving object detection in dynamic backgrounds remains a challenging problem. Our earlier work established that the background subtraction using the covariance matrix descriptor is robust for dynamic backgrounds. The work proposed herein extends this approach further, using just two features-Hu moment and intensity. An improved local Hu moment is proposed, where the moment calculation of a pixel, involving neighboring pixels, are used in a weighted manner to reduce the effects of background moving pixels and the accurate shape localization of moving objects simultaneously. To further counter the erratic labeling of dynamic pixels, the fact that the neighboring pixels are spatially correlated is exploited for model construction and foreground detection. An adaptive model updating rate is calculated as a function of model distance. The proposed approach models each pixel with a covariance matrix and a mean feature vector and is dynamically updated. Extensive studies are made with the proposed technique to demonstrate its effectiveness.