An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust object detection using a Radial Reach Filter (RRF)
Systems and Computers in Japan
Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Robust abandoned object detection using dual foregrounds
EURASIP Journal on Advances in Signal Processing
A fast algorithm for adaptive background model construction using parzen density estimation
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Hybrid Background Model Using Spatial-Temporal LBP
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Towards robust object detection: integrated background modeling based on spatio-temporal features
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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Background modeling has been widely researched to detect moving objects from image sequences. Most approaches have a falsenegative problem caused by a stopped object. When a moving object stops in an observing scene, it will be gradually trained as background since the observed pixel value is directly used for updating the background model. In this paper, we propose 1) a method to inhibit background training, and 2) a method to update an original background region occluded by stopped object. We have used probabilistic approach and predictive approach of background model to solve these problems. The great contribution of this paper is that we can keep paused objects from being trained.