A Bayesian Computer Vision System for Modeling Human Interactions
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
Robust Video-Based Surveillance by Integrating Target Detection with Tracking
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A self-organizing approach to detection of moving patterns for real-time applications
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Neural Network Approach to Background Modeling for Video Object Segmentation
IEEE Transactions on Neural Networks
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Background modeling is a preliminary processing task that generates background or reference frame for moving object detection. Apart from tracking background scene, a good quality background model will prevent false detection. Fuzzy Running Average (FRA) is an efficient background modeling scheme which employs a Fuzzy Inference System (FIS). Its high selectivity in background update prevents foreground object from appearing in the reference frame. Later, Extended Fuzzy Running Average (EFRA) was developed to allow FRA to recover the occlusion if a background object starts moving. However, the recovery rate of EFRA is limited due to the use of fixed width for detecting the occlusion's edge. In this paper, a newly developed method based on Scalable-Width Temporal Edge Detection (SWTED) is proposed to enhance the EFRA performance in locating and recovering the occlusion with higher rate. The results obtained show that the improved EFRA significantly outperforms FRA in background tracking. The algorithm is also well suited for real-time implementation.