Foreground region detection and tracking for fixed cameras

For real-time foreground detection on videos, probabilistic modeling for background and foreground colors are widely used. Stauffer and Grimson's model is very successful for foreground segmentation. In this method, each pixel is modeled independently with Gaussian mixtures. Explicit foreground probabilities for pixels are not calculated. Spatial and temporal continuity of pixels are omitted. In this thesis, we obtain foreground probabilities for the pixels using Stauffer and Grimson's model and apply hysteresis thresholding to utilize spatial continuity of pixels. For the same purpose, we also use Markov Random Field modeling and optimizations. To leverage the temporal continuity of pixels, mean-shift tracking is integrated into the segmentation to increase accuracy. Wherever applicable, we combine some of these improvements together. Our work shows that using the probabilistic approach with different enhancements results in much higher segmentation accuracy