Detecting anomalies in large number of moving objects

The need of detection of patterns and behaviors has been increasing in demand in the recent years as the quantity of moving objects rises. Examples of moving objects can be vehicles, human beings, animals or even vessels. By acquiring the positions of moving objects and analyzing them, we can find o...

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Main Author: He, Kelvin Zhenhao
Other Authors: Hsu Wen Jing
Format: Final Year Project
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/59130
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-591302023-03-03T20:47:28Z Detecting anomalies in large number of moving objects He, Kelvin Zhenhao Hsu Wen Jing School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis The need of detection of patterns and behaviors has been increasing in demand in the recent years as the quantity of moving objects rises. Examples of moving objects can be vehicles, human beings, animals or even vessels. By acquiring the positions of moving objects and analyzing them, we can find out the behaviors of the subjects (moving objects). Any behavior that deviates from the normal pattern can be used to interpret as urgent or even important to the subject. There are existing sources, reports on the geometric attributes of the positions, trajectories of moving objects; however the other important properties such as the semantics and the background geographical information are often left out. The objective of this FYP is to design and implement a program to do detection of patterns and moving objects anomalies from historical logs. The program will take in files containing geometric attributes of a human being and converting the data into a file that can be displayed onto Google Earth. Based on the current geometric position of the subject and the historical logs of previous travels, the program can detect any abnormal patterns and behaviors made by the subject. Bachelor of Engineering (Computer Science) 2014-04-23T12:06:10Z 2014-04-23T12:06:10Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/59130 en Nanyang Technological University 22 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
spellingShingle DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
He, Kelvin Zhenhao
Detecting anomalies in large number of moving objects
description The need of detection of patterns and behaviors has been increasing in demand in the recent years as the quantity of moving objects rises. Examples of moving objects can be vehicles, human beings, animals or even vessels. By acquiring the positions of moving objects and analyzing them, we can find out the behaviors of the subjects (moving objects). Any behavior that deviates from the normal pattern can be used to interpret as urgent or even important to the subject. There are existing sources, reports on the geometric attributes of the positions, trajectories of moving objects; however the other important properties such as the semantics and the background geographical information are often left out. The objective of this FYP is to design and implement a program to do detection of patterns and moving objects anomalies from historical logs. The program will take in files containing geometric attributes of a human being and converting the data into a file that can be displayed onto Google Earth. Based on the current geometric position of the subject and the historical logs of previous travels, the program can detect any abnormal patterns and behaviors made by the subject.
author2 Hsu Wen Jing
author_facet Hsu Wen Jing
He, Kelvin Zhenhao
format Final Year Project
author He, Kelvin Zhenhao
author_sort He, Kelvin Zhenhao
title Detecting anomalies in large number of moving objects
title_short Detecting anomalies in large number of moving objects
title_full Detecting anomalies in large number of moving objects
title_fullStr Detecting anomalies in large number of moving objects
title_full_unstemmed Detecting anomalies in large number of moving objects
title_sort detecting anomalies in large number of moving objects
publishDate 2014
url http://hdl.handle.net/10356/59130
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