Static and Moving Object Detection and Segmentation in Videos

This paper presents static object detection and segmentation method in videos. In this context, background subtraction BS technique based on the frame difference concept is applied to the identification of static objects. First, we estimate a frame differencing foreground mask by computing the difference of each frame with respect to a static reference frame image. The Mixture of Gaussian MOG method is applied to detect the moving particles and then outcome foreground mask is subtracted from frame differencing mask. Pre-processing techniques are applied to reduce the noise from the scene. Finally, morphological operation and largest connected component analysis are applied to segment the object. The proposed method was effectively validated with two public data sets. The results demonstrate the proposed approach can robustly detect, and segment the static objects without any prior information of tracking.