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
An HMM-Based Segmentation Method for Traffic Monitoring Movies
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
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Detecting Objects, Shadows and Ghosts in Video Streams by Exploiting Color and Motion Information
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A spatially distributed model for foreground segmentation
Image and Vision Computing
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
Enhancing the mixture of Gaussians background model with local matching and local adaptive learning
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Illumination invariant background model using mixture of gaussians and SURF features
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Finite asymmetric generalized Gaussian mixture models learning for infrared object detection
Computer Vision and Image Understanding
Journal of Mathematical Imaging and Vision
Integrated Computer-Aided Engineering
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Robust and real-time foreground segmentation is a crucial topic in many computer vision applications. Background subtraction is a typical approach to segment foreground 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. Our method is highly memory and time efficient. Moreover, it can effectively deal with the many scenes, such as the indoor scene, the outdoor scene, and the clutter scene. The experimental results show our method is efficient and effective.