Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Hybrid Background Model Using Spatial-Temporal LBP
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Hi-index | 0.00 |
Background subtraction is a typical approach to foreground segmentation by comparing each new frame with a learned model of the scene background in image sequences taken from a static camera. In this paper, we propose a flexible method to estimate the background model with the finite Gaussian mixture model. A stochastic approximation procedure is used to recursively estimate the parameters of the Gaussian mixture model, and to simultaneously obtain the asymptotically optimal number of the mixture components. The experimental results show our method is efficient and effective.