Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Free-viewpoint video of human actors
ACM SIGGRAPH 2003 Papers
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Type-2 fuzzy Gaussian mixture models
Pattern Recognition
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
Multivalued Background/Foreground Separation for Moving Object Detection
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Robust detection of moving objects in video sequences through rough set theory framework
Image and Vision Computing
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. To take into account this uncertainty, we propose to use a Type-2 Fuzzy Mixture of Gaussians Model. Results show the relevance of the proposed approach in presence of camera jitter, waving trees and water rippling.