Moving Object Detection and Tracking Based on Three-Frame Difference and Background Subtraction with Laplace Filter

Moving object detection and tracking is an important research field. Currently, ones of the core algorithms used for tracking include frame difference method (FD), background subtraction method (BS), and optical flow method. Here, authors are looking at the first two approaches since very adequate for very fast real-time treatments whereas optical flow has higher computation cost since related to a dense estimation. Combination of FD and BS with filters and edge detectors is a way to achieve sparse detection fast. This paper presents a tracking algorithm based on a new combination of FD and BS, using Canny edge detector and Laplace filter. Laplace filter occupies a leading role to sharpen the outlines and details. Canny edge detector identifies and extracts edge information. Morphology processing is used to eliminate interfering items finally. Experimental results show that 3FDBD-LC method has higher detection accuracy and better noise suppression than current combination methods on sequence images from standard datasets.