A new foreground segmentation method is presented in this paper for video analysis. Specifically, a new feature representation scheme is first proposed in different color spaces, namely, the RGB, the YIQ, and the YCbCr color spaces. The new feature vector, which integrates the color values in a particular color space, the horizontal and vertical Haar wavelet features, and the temporal difference features, enhances the discriminatory power. A new Global Foreground Modeling (GFM) method is then presented to improve upon the popular video analysis approaches. The Bayes classifier is finally applied for foreground segmentation in video. Experimental results using the New Jersey Department of Transportation (NJDOT) traffic video sequences show that the new foreground segmentation method achieves better performance than the popular video analysis methods.