Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Flexible background mixture models for foreground segmentation
Image and Vision Computing
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The Mixture of Gaussians (MoG) is a frequently used method for foreground-background separation. Although it is quite capable of handling gradual illumination changes and multi-model background, it cannot cope with dynamic changes such as the presence of paused objects, shadows, and sudden illumination changes. Furthermore, it is a parametric model and in general, its parameter tuning for different scenes remains a manual effort. In this paper, we propose an online learning framework that tackles these issues. Our main contributions are: local adaptive parameter learning, a feedback based updating method for stopped objects, hierarchical SURF features matching based ghosts suppression, and a new sudden illumination detection and handling technique. The proposed model is rigorously tested and compared with several previous models and has shown significant performance improvements.