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Monocular viewpoint invariant human activity recognition

One of the grand goals of robotics is to have assistive robots living side-by-side with humans, autonomously assisting humans in everyday activities. To be able to interact with humans and assist them, robots must be able to understand and interpret human activities. There is a growing interest in t...

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Main Authors: Htike@Muhammad Yusof, Zaw Zaw, Egerton, Simon, Kuang, Ye Chow
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
AI Indexes (General)
Online Access:http://irep.iium.edu.my/43201/1/CIS-RAM_2011.PDF
http://irep.iium.edu.my/43201/
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070449
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spelling my.iium.irep.432012015-06-08T03:32:30Z http://irep.iium.edu.my/43201/ Monocular viewpoint invariant human activity recognition Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow AI Indexes (General) One of the grand goals of robotics is to have assistive robots living side-by-side with humans, autonomously assisting humans in everyday activities. To be able to interact with humans and assist them, robots must be able to understand and interpret human activities. There is a growing interest in the problem of human activity recognition. Despite much progress, most computer vision researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. However, since the robots and humans are free to move around in the environment, the viewpoint of a robot with respect to a person varies all the time. Therefore, we attempt to relax the infamous fixed viewpoint assumption and present a novel and efficient framework to recognize and classify human activities from monocular video source from arbitrary viewpoint. The proposed framework comprises of two stages: human pose recognition and human activity recognition. In the pose recognition stage, an ensemble of pose models performs inference on each video frame. Each pose model estimates the probability that the given frame contains the corresponding pose. Over a sequence of frames, each pose model forms a time series. In the activity recognition stage, we use nearest neighbor, with dynamic time warping as a distance measure, to classify pose time series. We have built a small-scale proof-of-concept model and performed some experiments on three publicly available datasets. The satisfactory experimental results demonstrate the efficacy of our framework and encourage us to further develop a full-scale architecture. 2011-09-17 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/43201/1/CIS-RAM_2011.PDF Htike@Muhammad Yusof, Zaw Zaw and Egerton, Simon and Kuang, Ye Chow (2011) Monocular viewpoint invariant human activity recognition. In: IEEE Conference on Robotics, Automation and Mechatronics (RAM), 17-19 September 2011, Qingdao, China. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070449
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic AI Indexes (General)
spellingShingle AI Indexes (General)
Htike@Muhammad Yusof, Zaw Zaw
Egerton, Simon
Kuang, Ye Chow
Monocular viewpoint invariant human activity recognition
description One of the grand goals of robotics is to have assistive robots living side-by-side with humans, autonomously assisting humans in everyday activities. To be able to interact with humans and assist them, robots must be able to understand and interpret human activities. There is a growing interest in the problem of human activity recognition. Despite much progress, most computer vision researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. However, since the robots and humans are free to move around in the environment, the viewpoint of a robot with respect to a person varies all the time. Therefore, we attempt to relax the infamous fixed viewpoint assumption and present a novel and efficient framework to recognize and classify human activities from monocular video source from arbitrary viewpoint. The proposed framework comprises of two stages: human pose recognition and human activity recognition. In the pose recognition stage, an ensemble of pose models performs inference on each video frame. Each pose model estimates the probability that the given frame contains the corresponding pose. Over a sequence of frames, each pose model forms a time series. In the activity recognition stage, we use nearest neighbor, with dynamic time warping as a distance measure, to classify pose time series. We have built a small-scale proof-of-concept model and performed some experiments on three publicly available datasets. The satisfactory experimental results demonstrate the efficacy of our framework and encourage us to further develop a full-scale architecture.
format Conference or Workshop Item
author Htike@Muhammad Yusof, Zaw Zaw
Egerton, Simon
Kuang, Ye Chow
author_facet Htike@Muhammad Yusof, Zaw Zaw
Egerton, Simon
Kuang, Ye Chow
author_sort Htike@Muhammad Yusof, Zaw Zaw
title Monocular viewpoint invariant human activity recognition
title_short Monocular viewpoint invariant human activity recognition
title_full Monocular viewpoint invariant human activity recognition
title_fullStr Monocular viewpoint invariant human activity recognition
title_full_unstemmed Monocular viewpoint invariant human activity recognition
title_sort monocular viewpoint invariant human activity recognition
publishDate 2011
url http://irep.iium.edu.my/43201/1/CIS-RAM_2011.PDF
http://irep.iium.edu.my/43201/
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070449
_version_ 1643612342681862144
score 13.211869

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