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MULTIPLE PEDESTRIANS TRACKING BY DISCRIMINATIVE MODELS
by
Cheng-Hao Kuo
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
December 2011
Copyright 2012 Cheng-Hao Kuo
Object Description
| Title | Multiple pedestrians tracking by discriminative models |
| Author | Kuo, Cheng-Hao |
| Author email | samuelkuo@gmail.com;cheng-hao.kuo@siemens.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-04-27 |
| Date submitted | 2011-11-10 |
| Date approved | 2011-11-11 |
| Restricted until | 2011-11-11 |
| Date published | 2011-11-11 |
| Advisor (committee chair) | Nevatia, Ram |
| Advisor (committee member) |
Medioni, Gerard Leahy, Richard |
| Abstract | We present our work on multiple pedestrians tracking in a single camera and across multiple non-overlapping cameras. We propose an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance model, which is the key element for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which do not resolve ambiguities between the different targets. We propose an algorithm for learning discriminative appearance models for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an AdaBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches. ❧ Furthermore, we extend our approach to multiple non-overlapping cameras. Given the multi-target tracking results in each camera, we propose a framework to associate those tracks. Collecting reliable training samples is a major challenge in on-line learning since supervised correspondence is not available at runtime. To alleviate the inevitable ambiguities in these samples, Multiple Instance Learning (MIL) is applied to learn an appearance affinity model which effectively combines three complementary image descriptors and their corresponding similarity measurements. Based on the spatial-temporal information and the proposed appearance affinity model, we present an improved inter-camera track association framework to solve the ""target handover"" problem across cameras. Our evaluations indicate that our method has higher discrimination between different targets than previous methods. |
| Keyword | adaboost; association-based tracking; detection-based tracking; discriminative models; multiple instance learning; mutli-target 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 |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Kuo, Cheng-Hao |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume71/etd-KuoChengHa-399.pdf |
Description
| Title | Page 1 |
| Full text | MULTIPLE PEDESTRIANS TRACKING BY DISCRIMINATIVE MODELS by Cheng-Hao Kuo A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) December 2011 Copyright 2012 Cheng-Hao Kuo |
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