Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Pattern Recognition Letters
Foreground objects segmentation for moving camera scenarios based on SCGMM
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Journal of Visual Communication and Image Representation
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In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/ background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground is modeled using a Gaussian Mixture Model with feature vectors consisting of the spatial (x, y) and colour (r, g, b) components. The spatial components of this model are updated using the Expectation Maximization algorithm after the classification of each frame. The background model is formulated in the 5 dimensional feature space in order to be able to apply a Maximum A Posteriori framework for the classification. The classification is done using a graph cut algorithm that allows taking into account neighborhood information. The results presented in the paper show the improvement of the system in situations where the foreground objects have similar colors to those of the background.