A New Algorithm for Improving Basic Model Based Foreground Detection Using Neutrosophic Similarity Score
Year: 2017
Type: article
Abstract: Foreground detection is a task for detecting the moving objects in the scene like in video surveillance.Several basic background models are often used due to their high efficiency.However, their results are not good when there exists noisy information generated by the bad weather, camera jitter, etc. Neutrosophic set (NS) is as a new branch of philosophy dealing with the origin, nature and scope of neutralities.It has an inherent ability to handle the indeterminant information like the noise included in images and video sequences.Till now, NS has been successfully applied into many computer vision research fields, such as image segmentation and skeleton extraction.In this paper, we utilize the NS to improve the robustness of the basic background model.First, a sequence of background subtraction images is calculated.Second, such a sequence is represented in the NS domain via three membership subsets T, I, and F. Finally, a neutrosophic similarity score (NSS) is defined to highlight the moving objects on the image plane.Then the Otsu's method is employed to determine an optimized value for extracting the area of the moving objects.Experiments are conducted on a variety of realworld video sequences, and the experimental results demonstrate that the proposed approach can extract the moving objects robustly.
Institution: Shaoxing University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2765151371