Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
The Dense Estimation of Motion and Appearance in Layers
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 11 - Volume 11
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using EM algorithm by histogram
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
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Extracting foreground objects is an important task in many video processing/analysis systems. In this paper, we propose a technique for foreground object extraction, under static camera condition. In our approach the spatial histogram of a single background image is modeled as Mixture of Gaussians and this model is updated after every few frames. To extract the foreground, input frames are compared with current background frame model and foreground pixels are classified according to intensity differences. To mitigate the errors caused due to movement of the background objects (e.g tree leaves in outdoor scenes), we also incorporate optical flow in an efficient manner. We demonstrate performance of our approach on various indoor and outdoor scenes.