Abstract
This thesis is focused on the problem of moving object detection. It is not only to detect the moving object but also to detect the object shadow areas. Generally speaking, the most common and basic approach to detect the moving object is background subtraction. Traditional background subtraction methods work under the assumption that the background is stationary. However, it is not applicable to dynamic background, whose background image changes over time. In this thesis, we propose an adaptive and local mixture-of-Gaussians model for each pixel to build the background model. We modify the original Gaussian Mixture Model (GMM) to the Local-Patch Gaussian Mixture Model (LPGMM). Thus, the LPGMM is utilized to solve the problem of detecting the moving object under dynamic background. Most traditional background subtraction methods in dynamic background do not consider the problem of cast shadow. Since the object shadow moves with the moving object, it is difficult to differentiate moving shadow from moving objects under dynamic background. We use the support vector machine (SVM) to detect cast shadow areas under the dynamic background environment.