Date of Award

Spring 2013

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Chung-Hao Chen

Committee Member

Gene Hou

Committee Member

Jiang Li

Call Number for Print

Special Collections LD4331.E55 R42 2013

Abstract

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region or frame level and designating any elements that deviate from the background model as foreground. Most existing approaches are capable of segmenting a number of distinct components, but are unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. Additionally, current state-of-the-art segmentation techniques treat objects and background as separate entities, ignoring any interaction between an object and the background. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes, Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on five sample images from two complex data sets demonstrate an average F-measure improvement of 0.643103 per image exhibiting a significant improvement in accuracy over a state-of-the-art video segmentation technique.

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DOI

10.25777/9n7x-ma03

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