Faculty Advisor or Committee Member

Xinming Huang, Advisor

Faculty Advisor or Committee Member

Jie Fu, Committee Member

Faculty Advisor or Committee Member

Yanhua Li, Committee Member

Identifier

etd-042516-145129

Abstract

Pedestrian detection is a canonical instance of object detection that remains a popular topic of research and a key problem in computer vision due to its diverse applications. These applications have the potential to positively improve the quality of life. In recent years, the number of approaches to detecting pedestrians in monocular and binocular images has grown steadily. However, the use of multispectral imaging is still uncommon. This thesis work presents a novel approach to data and feature fusion of a multispectral imaging system for pedestrian detection. It also includes the design and building of a test rig which allows for quick data collection of real-world driving. An application of the mathematical theory of trifocal tensor is used to post process this data. This allows for pixel level data fusion across a multispectral set of data. Performance results based on commonly used SVM classification architectures are evaluated against the collected data set. Lastly, a novel cascaded SVM architecture used in both classification and detection is discussed. Performance improvements through the use of feature fusion is demonstrated.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Electrical & Computer Engineering

Project Type

Thesis

Date Accepted

2016-04-25

Accessibility

Unrestricted

Subjects

pedestrian detection, data fusion, trifocal tensor, feature fusion, decision fusion, stereo vision, thermal vision

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