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MULTIPLE VEHICLE SEGMENTATION AND TRACKING IN COMPLEX ENVIRONMENTS
by
Xuefeng Song
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2007
Copyright 2007 Xuefeng Song
Object Description
| Title | Multiple vehicle segmentation and tracking in complex environments |
| Author | Song, Xuefeng |
| Author email | xsong@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2006-11-15 |
| Restricted until | Unrestricted |
| Advisor (committee chair) | Nevatia, Ram |
| Advisor (committee member) |
Medioni, Gerard G. Cohen, Isaac Lu, Zhonglin |
| Abstract | Our goal is to detect and to track multiple moving vehicles observed from static surveillance cameras, which are usually placed on poles or buildings. Methods of background subtraction are widely used in these kinds of conditions. But to extract vehicle information from motion foreground, common difficulties, such as noise foreground, shadow, scene occlusion, blob merge and blob split, have to be solved. By using vehicle shape models, in addition to camera calibration and ground plane knowledge, the proposed methods can detect, track and classify moving vehicles in the presence of all these difficulties.; Two methods are proposed in this thesis to deal with related problems. The first method uses dynamic background model to extract the motion foreground. The models of camera and vehicle are used to reduce the foreground noise. Spatial and temporal constraints are applied to handle blob split, and object color appearance is used to track each vehicle when multiple vehicles are merged together. Evaluation on a large dataset by a third party shows that this method works robustly under many conditions.; The second method focuses on challenging tracking situations where vehicle inter-occlusion is prevalent and persistent. In this case, each foreground blob can contain multiple vehicles. Simple one-to-one correspondence between the foreground blobs and vehicles does not apply any more. Segmentation of the merged vehicles is a difficult problem. This proposed method works in the framework of Markov chain Monte Carlo (MCMC) approach. By sampling in the multi-vehicle configuration space, the method searches for the set of vehicle parameters, that best explains the foreground. Several bottom-up detections are utilized with top-down analysis to guide the sampling in an effective way.; The goal of this work is to infer the trajectory of each individual vehicle. Because of the approximation of vehicle models and the limitation of the likelihood function, the multi-vehicle configuration with the highest probability may not always be the correct segmentation. By exploring the spatial and temporal constraints across the image sequences, a tracking method is proposed to reduce the errors on single frame vehicle detection. |
| Keyword | computer vision; pattern recognition; vehicle tracking |
| 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 |
| Type | texts |
| Legacy record ID | usctheses-m308 |
| Rights | Song, Xuefeng |
| 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-Song-20070304 |
| Archival file | uscthesesreloadpub_Volume23/etd-Song-20070304.pdf |
Description
| Title | Page 1 |
| Full text | MULTIPLE VEHICLE SEGMENTATION AND TRACKING IN COMPLEX ENVIRONMENTS by Xuefeng Song A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) May 2007 Copyright 2007 Xuefeng Song |
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