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MONOCULAR HUMAN POSE TRACKING AND ACTION RECOGNITION
IN DYNAMIC ENVIRONMENTS
by
Vivek Kumar Singh
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
Copyright 2011
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2011
Vivek Kumar Singh
Object Description
| Title | Monocular human pose tracking and action recognition in dynamic environments |
| Author | Singh, Vivek Kumar |
| Author email | vivekks@gmail.com;vivek-singh@siemens.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-04-28 |
| Date submitted | 2011-10-05 |
| Date approved | 2011-10-05 |
| Restricted until | 2011-10-05 |
| Date published | 2011-10-05 |
| Advisor (committee chair) | Nevatia, Ramakant |
| Advisor (committee member) |
Medioni, Gerard Ortega, Antonio |
| Abstract | The objective of this work is to develop an efficient method to find human in videos captured from a single camera, and recognize the action being performed. Automatic detection of humans in a scene and understanding the ongoing activities has been extensively studied, as solution to this problem finds applications in diverse areas such as surveillance, video summarization, content mining and human computer interaction, among others. ❧ Though significant advances have made towards finding human in specific poses such as upright pose in cluttered scenes, the problem of finding a human in an arbitrary pose in an unknown environment is still a challenge. We address the problem of estimating human pose using a part based approach, that first finds body part candidates using part detectors and then enforce kinematic constraints using a tree-structured graphical model. For inference, we present a collaborative branch and bound algorithm that uses branch and bound method to search for each part and use kinematics from neighboring parts to guide the branching behavior and compute bounds on the best part estimate. We use multiple, heterogeneous part detectors with varying accuracy and computation requirements, ordered in a hierarchy, to achieve more accurate and efficient pose estimation. ❧ While the above approach deals well with pose articulations, it still fails to find human in poses with heavy self occlusion such as crouch, as it does not model inter part occlusion. Thus, recognizing actions from inferred poses would be unreliable. In order to deal with this issue, we propose a joint tracking and recognition approach which tracks the actor pose by sampling from 3D action models and localizing each pose sample; this also allows view-invariant action recognition. We model an action as a sequence of transformations between keyposes. These action models can be obtained by annotating only a few keyposes in 2D; this avoids large training data and MoCAP. For efficiently localizing a sampled pose, we generate a Pose-Specific Part Model (PSPM) which captures appropriate kinematic and occlusion constraints in a tree-structure. In addition, our approach also does not require pose silhouettes and thus also works well in presence of background motion. We show improvements to previous results on two publicly available datasets as well as on a novel, augmented dataset with dynamic backgrounds. ❧ Since the poses are sampled from action models, the above activity driven approach works well if the actor only performs actions for which models are available, and does not generalize well to unseen poses and actions. We address this by proposing an activity assisted tracking framework that combines the activity driven tracking with the bottom up pose estimation by using pose samples obtained using part models, in addition to those sampled from action models. We demonstrate the effectiveness of our approach on long video sequences with hand gestures. |
| Keyword | pictorial structures; branch and bound; conditional random fields; particle filtering; |
| 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 |
| Contributing entity | University of Southern California |
| Rights | Singh, Vivek Kumar |
| 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_Volume6/etd-SinghVivek-318.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Full text | MONOCULAR HUMAN POSE TRACKING AND ACTION RECOGNITION IN DYNAMIC ENVIRONMENTS by Vivek Kumar Singh A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree Copyright 2011 DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) December 2011 Vivek Kumar Singh |
