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Dissertation/Thesis Abstract

Video segmentation into background and foreground using simplified mean-shift filter and clustering
by Sinha, Sudhanshu Kumar, D.S., Bowie State University, 2014, 127; 3665509
Abstract (Summary)

Video Segmentation decomposes image frames into background and foreground. In this dissertation, a combination of simplified mean-shift filter and clustering are used in modeling the background. After computing the mean shift values, a histogram clustering is applied and then each test pixel is compared with each cluster. The most common models used for background estimation are mixture of Gaussian (MOG), Kernel Density Estimation (KDE), etc. Comparison of the proposed approach with some of the aforementioned models have been made and it was observed that a relatively simple model using a simplified Mean-shift computation and Histogram clustering can produce results that are comparable to those obtained by other methods. The proposed approach was tested on video data obtained from Wallflower test images from its source website and also the video data captured from Bowie State University cameras. The results are encouraging and show the validity of this approach for background modeling.

Indexing (document details)
Advisor: Mareboyana, Manohar
Commitee: Cole-Rhodes, Arlene A., Ji, Soo-Yeon, Mareboyana, Manohar, Srivastava, Sadanand, Yang, Bo
School: Bowie State University
Department: Computer Science
School Location: United States -- Maryland
Source: DAI-B 76/04(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Background subtraction, Clustering, Foreground detection, Meanshift, Segmentation, Video
Publication Number: 3665509
ISBN: 978-1-321-38097-2
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