Pedestrian detection in EO and IR video

Abstract

The task of determining the types of objects present in the scene, or object recognition is one of the fundamental problems of computer vision. Applications include, medical imaging, security, and multi-media database search. For example, before attempting to detect suspicious behavior, an automated surveillance system would have to determine the classes of objects that are attempting to interact. This task is adversely affected by poor quality video or images. For my thesis I addressed the problem of differentiating between pedestrians and vehicles in both Infra Red and Electro Optical videos. The problem was made quite difficult by the targets: small size, poor quality of the video as well as the precision of the moving target indicator algorithm. However combining the inverse wavelet transform (IDWI) for feature extraction and the Support Vector Machine (SVM) for actual classification provided results superior to other features, and machine learning techniques.

Notes

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Thesis Completion

2006

Semester

Spring

Advisor

Shah, Mubarak

Degree

Bachelor of Science (B.S.)

College

College of Engineering and Computer Science

Degree Program

Computer Science

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Format

Print

Identifier

DP0022082

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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