Type
Text
Type
Thesis
Advisor
Doboli, Alex | Hong, Sangjin.
Date
2010-12-01
Keywords
classification, clustering, ontology, scene understanding, sound localization | Computer engineering
Department
Department of Computer Engineering
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71384
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
The thesis is about an embedded system application aimed at identifying the semantics of traffic based on acoustic data. Sound localization, classification and clustering are used for scene understanding. The report presents a set of experiments used to simulate different traffic scenarios. An alternative implementation for sound localization is also explored, where fixed point representation of rational numbers is used instead of floating point numbers. The results for both the implementations are compared in terms of execution speed and accuracy for a Programmable System-on-Chip (PSoC). | 43 pages
Recommended Citation
Rajagopal, Shreyas Kodasara, "Traffic Scene Understanding using Sound-based Localization, SVM Classification and Clustering" (2010). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 590.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/590