Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Illumination-Invariant Change Detection Using a Statistical Colinearity Criterion
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Robust Multiple Car Tracking with Occlusion Reasoning
Robust Multiple Car Tracking with Occlusion Reasoning
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
GMM-based efficient foreground detection with adaptive region update
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Background subtraction is an essential task in several static camera based computer vision systems. Background modeling is often challenged by spatio-temporal changes occurring due to local motion and/or variations in illumination conditions. The background model is learned from an image sequence in a number of stages, viz. preprocessing, pixel/region feature extraction and statistical modeling of feature distribution. A number of algorithms, mainly focusing on feature extraction and statistical modeling have been proposed to handle the problems and comparatively little exploration has occurred at the preprocessing stage. Motivated by the fact that disturbances caused by local motions disappear at lower resolutions, we propose to represent the images at multiple scales in the preprocessing stage to learn a pyramid of background models at different resolutions. During operation, foreground pixels are detected first only at the lowest resolution, and only these pixels are further analyzed at higher resolutions to obtain a precise silhouette of the entire foreground blob. Such a scheme is also found to yield a significant reduction in computation. The second contribution in this paper involves the use of the co-linearity statistic (introduced by Mester et al. for the purpose of illumination independent change detection in consecutive frames) as a pixel neighborhood feature by assuming a linear model with a signal modulation factor and additive noise. The use of co-linearity statistic as a feature has shown significant performance improvement over intensity or combined intensity-gradient features. Experimental results and performance comparisons (ROC curves) for the proposed approach with other algorithms show significant improvements for several test sequences.