Dissertation/Thesis Abstract

Object Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems
by Reza, Tasmia, M.S., Mississippi State University, 2018, 77; 10841471
Abstract (Summary)

A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three-dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.

Indexing (document details)
Advisor: Ball, John E.
Commitee: Anderson, Derek T., Tang, Bo
School: Mississippi State University
Department: Electrical and Computer Engineering
School Location: United States -- Mississippi
Source: MAI 58/01M(E), Masters Abstracts International
Subjects: Computer Engineering, Electrical engineering, Artificial intelligence
Keywords: Advanced driver assistance systems, Convolutional neural networks, LiDAR, Support vector machine
Publication Number: 10841471
ISBN: 978-0-438-31380-4
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