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Using unlabeled 3D motion examples for human activity understanding Gupta, Ankur
Abstract
We demonstrate how a large collection of unlabeled motion examples can help us in understanding human activities in a video. Recognizing human activity in monocular videos is a central problem in computer vision with wide-ranging applications in robotics, sports analysis, and healthcare. Obtaining annotated data to learn from videos in a supervised manner is tedious, time-consuming, and not scalable to a large number of human actions. To address these issues, we propose an unsupervised, data-driven approach that only relies on 3d motion examples in the form of human motion capture sequences. The first part of the thesis deals with adding view-invariance to the standard action recognition task, i.e., identifying the class of activity given a short video sequence. We learn a view-invariant representation of human motion from 3d examples by generating synthetic features. We demonstrate the effectiveness of our method on a standard dataset with results competitive to the state of the art. Next, we focus on the problem of 3d pose estimation in realistic videos. We present a non-parametric approach that does not rely on a motion model built for a specific action. Thus, our method can deal with video sequences featuring multiple actions. We test our 3d pose estimation pipeline on a challenging professional basketball sequence.
Item Metadata
Title |
Using unlabeled 3D motion examples for human activity understanding
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2016
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Description |
We demonstrate how a large collection of unlabeled motion examples can help us in understanding human activities in a video. Recognizing human activity in monocular videos is a central problem in computer vision with wide-ranging applications in robotics, sports analysis, and healthcare. Obtaining annotated data to learn from videos in a supervised manner is tedious, time-consuming, and not scalable to a large number of human actions. To address these issues, we propose an unsupervised, data-driven approach that only relies on 3d motion examples in the form of human motion capture sequences.
The first part of the thesis deals with adding view-invariance to the standard action recognition task, i.e., identifying the class of activity given a short video sequence. We learn a view-invariant representation of human motion from 3d examples by generating synthetic features. We demonstrate the effectiveness of our method on a standard dataset with results competitive to the state of the art. Next, we focus on the problem of 3d pose estimation in realistic videos. We present a non-parametric approach that does not rely on a motion model built for a specific action. Thus, our method can deal with video sequences featuring multiple actions. We test our 3d pose estimation pipeline on a challenging professional basketball sequence.
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Genre | |
Type | |
Language |
eng
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Date Available |
2016-07-14
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0305862
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2016-09
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Campus | |
Scholarly Level |
Graduate
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DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International