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FROM RAW SENSOR DATA TO MOVING OBJECT TRAJECTORIES
AT RIGHT RESOLUTION, QUALITY, AND ABSTRACTION
by
Hyunjin Yoon
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2009
Copyright 2009 Hyunjin Yoon
Object Description
| Title | From raw sensor data to moving object trajectories at right resolution, quality, and abstraction |
| Author | Yoon, Hyunjin |
| Author email | hjy@usc.edu; hyunjin.yoon@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-09-24 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-11-18 |
| Advisor (committee chair) | Shahabi, Cyrus |
| Advisor (committee member) |
Nakano, Aiichiro Winstein, Carolee J. |
| Abstract | A moving object trajectory is a series of locations of a moving object sampled at discrete instances of time. Real-world moving object trajectories acquired by location-aware sensors typically involve a large number of moving objects, massive observations, and noisy measurements. In addition, the trajectory data is available only at the form of bulky point clouds of sampled locations and timestamps. Such raw sensor data is therefore neither at right resolution and quality nor at right level of abstraction for the efficient data exploration, advanced data analysis, and high-level decision making.; In this dissertation, we address the problem of large gap between the raw sensor data that is readily available and the desired trajectory data that is needed. Specifically, we focus on bridging the gap in two perspectives: 1) transforming the raw sensor data into the trajectory data approximated at right resolution and quality by trajectory segmentation and 2) summarizing a large set of trajectory data at right level of abstraction by discovering a specific type of movement patterns called a valid convoy.; We first propose a preprocessing technique, called robust time-referenced trajectory segmentation, to transform the raw sensor data into the desired trajectory data approximated at right resolution and quality. Unlike conventional trajectory segmentation techniques focusing only on the spatial features of the movement and vulnerable to outliers, our proposed trajectory segmentation methods take into account both geo-spatial and temporal structures of movement to obtain spatially and temporally homogeneous segments and are also robust against to time-referenced spatial outliers.; In addition, we discover a specific type of movement patterns called a valid convoy to summarize the interesting mobility of moving objects both in space and time at the desired level of abstraction. Existing convoy discovery algorithms have a critical problem of accuracy; they tend to both miss larger convoy patterns and retrieve invalid ones. We therefore propose two new valid convoy discovery algorithms, a straightforward VCoDA and an efficient alternative EVCoDA, which accurately discover all valid convoys from moving object trajectories. Our extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our trajectory segmentation and convoy mining techniques. |
| Keyword | moving object trajectories; trajectory segmentation; convoy patten mining |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2744 |
| Rights | Yoon, Hyunjin |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Yoon-3332 |
| Archival file | uscthesesreloadpub_Volume56/etd-Yoon-3332.pdf |
Description
| Title | Page 1 |
| Full text | FROM RAW SENSOR DATA TO MOVING OBJECT TRAJECTORIES AT RIGHT RESOLUTION, QUALITY, AND ABSTRACTION by Hyunjin Yoon A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) December 2009 Copyright 2009 Hyunjin Yoon |
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