Object Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems

Author

Tasmia Reza

Issuing Body

Mississippi State University

Advisor

Ball, John E.

Committee Member

Tang, Bo

Committee Member

Anderson, Derek T.

Date of Degree

8-10-2018

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

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.

URI

https://hdl.handle.net/11668/19880

Comments

Advanced Driver Assistance Systems||Support Vector Machine||LiDAR||Convolutional Neural Networks

Recommended Citation

Reza, Tasmia, "Object Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems" (2018). Theses and Dissertations. 3341.
https://scholarsjunction.msstate.edu/td/3341