Efficient Method for Moving Object Detection in Cluttered Background Using Gaussian Mixture Model

Foreground object detection in video is a fundamental step for automated video surveillance system and many computer vision applications. Mostly moving foreground object is detected by background subtraction techniques. In dynamic background, Gaussian Mixture Model (GMM) performs better for object detection. In this work, a GMM based Basic Background Subtraction (BBS) model is used for background modeling. The connected component and blob labeling has been used to improve the model with a threshold. Morphological operators are used to improve the foreground information with a suitable structure element. The experimental study shows that the proposed work performs better in comparison to considered state-of-the-art methods in term of error.