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MULTIPLE HUMANS TRACKING BY LEARNING APPEARANCE AND
MOTION PATTERNS
by
Bo Yang
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)
August 2012
Copyright 2012 Bo Yang
Object Description
| Title | Multiple humnas tracking by learning appearance and motion patterns |
| Author | Yang, Bo |
| Author email | yangbo@usc.edu;bo.yang02@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-04-23 |
| Date submitted | 2012-07-30 |
| Date approved | 2012-07-31 |
| Restricted until | 2012-07-31 |
| Date published | 2012-07-31 |
| Advisor (committee chair) | Nevatia, Ram |
| Advisor (committee member) |
Medioni, Gerard Kuo, C.C. Jay |
| Abstract | Tracking multiple humans in real scenes is an important problem in computer vision due to its importance for many applications, such as surveillance, robotics, and human-computer interactions. Association based tracking often achieves better performances than other approaches in crowded scenes. Based on this framework, I propose offline and online learning algorithms to automatically find potential useful appearance and motion patterns, and utilize them to deal with difficulties in the association framework and to produce much better tracking results. ❧ In association based framework, an offline learned detector is first applied in each video frame to produce detection responses, which are further associated into tracklets, i.e., track fragments, in multiple steps. Measurement of affinities between tracklets is the key issue that determines the performance. In the first part of my thesis, I propose an online learning algorithm which automatically find three important cues from a static scene to improve tracking performance: non-linear motion patterns, potential entry/exit points, and co-moving groups. ❧ Association based tracking methods are often based on the assumption that affinities between tracklet pairs are independent of each other. However, this is not always true in real cases. In order to relax the independent assumption, we introduce an offline learned Conditional Random Field (CRF) model to estimate both affinities between tracklets and dependencies among them. Finding best associations between tracklets is transformed into an energy minimization problem, and energies of unary and pairwise terms in the CRF model are offline learned from pre-labeled ground truth data by a RankBoost algorithm. Then I further extended the approach into an online version. Positive and negative pairs are online collected according to temporal constraints; the learned appearance models better distinguish close but visually similar targets and the learned motion models considered relative distances between targets to alleviate camera motion and non-linear path effects. ❧ As detection performance limits the performances of traditional association based tracking approaches, I further propose an online learned discriminative part-based appearance models which incorporates category free tracking techniques into association based tracking. In this work, occlusions among targets are explicitly considered to produce more robust appearance models. A category free tracking method is adopted to track a target without detection responses while distinguishing different targets and the background. ❧ I designed comprehensive experiments to evaluate all my algorithms and important modules. The performances show effectiveness of my approaches on different data sets, with different human densities, illuminations, camera motions, and etc. |
| Keyword | multi-target tracking; appearance and motion patterns |
| 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 | Yang, Bo |
| 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_Volume4/etd-YangBo-1077.pdf |
Description
| Title | Page 1 |
| Full text | MULTIPLE HUMANS TRACKING BY LEARNING APPEARANCE AND MOTION PATTERNS by Bo Yang 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) August 2012 Copyright 2012 Bo Yang |
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