Date of Award

12-2014

Document Type

Thesis

Department

Computer Science

First Advisor

Dr. Qiuming Zhu

Second Advisor

Dr. Haifeng Guo

Third Advisor

Dr. William Mahoney

Abstract

Video surveillance systems play an important role in many civilian and military applications, for the purposes of security and surveillance. Object detection is an important component in a video surveillance system, used to identify possible objects of interest and to generate data for tracking and analysis purposes. Not much exploration has been done to track the moving parts of the object which is being tracked. Some of the promising techniques like Kalman Filter, Mean-shift algorithm, Matching Eigen Space, Discrete Wavelet Transform, Curvelet Transform, Distance Metric Learning have shown good performance for keeping track of moving object.

Most of this work is focused on studying and analyzing various object tracking techniques which are available. Most of the techniques which are available for object tracking have heavy computation requirements. The intention of this research is to design a technique, which is not computationally intensive and to be able to track relative movements of object parts in real time. The research applies a technique called foreground detection (also known as background subtraction) for tracking the object as it is not computationally intensive. For tracking the relative movement of object parts, a skeletonization technique is used. During implementation, it is found that using skeletonization technique, it is harder to extract the objects parts.

Comments

A Thesis Presented to the Department of Computer Science and the Faculty of the Graduate College University of Nebraska In Partial Fulfillment of the Requirements for the Degree Master of Science University of Nebraska at Omaha. Copyright 2014 Praneeth Talluri.

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