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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Bibliographic Details
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/ http://irep.iium.edu.my/43201/ http://irep.iium.edu.my/43201/1/CIS-RAM_2011.PDF

id	iium-43201
recordtype	eprints
spelling	iium-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 PeerReviewed 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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
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/ http://irep.iium.edu.my/43201/ http://irep.iium.edu.my/43201/1/CIS-RAM_2011.PDF
first_indexed	2023-09-18T21:01:34Z
last_indexed	2023-09-18T21:01:34Z
_version_	1777410658046836736

Monocular viewpoint invariant human activity recognition

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