Statistical background modelling for tracking with a virtual camera
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Efficient nonparametric kernel density estimation for real time computer vision
Efficient nonparametric kernel density estimation for real time computer vision
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Probabilistic Models for Video Object Segmentation and Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A novel background generation method based on nonparametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. Based on ECD, an effective background generation method, most reliable background mode (MRBM), is developed. The basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. The advantages of this method are threefold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. Extensive experimental results illustrate its good performance.