Robust object detection using a Radial Reach Filter (RRF)
Systems and Computers in Japan
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
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
A novel adaptive gaussian mixture model for background subtraction
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Adaptive background modeling for paused object regions
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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Background Modeling has been widely researched to detect moving objects from image sequences. It is necessary to adapt the background model various changes of illumination condition. Recent years, a hybrid type of background model which consists of more than one background model has been used for object detection since it is very robust for illumination changes. In this paper, we also propose a new hybrid type of background model named "Hybrid Spatial-Temporal Background Model". Our model consists of two different kinds of background models. One is pixellevel background model which is robust for long-term illumination changes. The other is spatial-temporal background model which is robust for short-term illumination changes. Our experimental results demonstrate superiority of our method to some related works.