NDLI: Enhancing human action recognition through spatio-temporal feature learning and semantic rules

Content Provider	IEEE Xplore Digital Library
Author	Ramirez-Amaro, Karinne Eun-Sol Kim Jiseob Kim Byoung-Tak Zhang Beetz, Michael Cheng, Gordon
Copyright Year	2013
Description	Author affiliation: School of Computer Science and Engineering, Seoul National University, South Korea (Eun-Sol Kim; Jiseob Kim; Byoung-Tak Zhang) \|\| Institute for Artificial Intelligence, University of Bremen, Germany (Beetz, Michael) \|\| Faculty of Electrical Engineering, Institute for Cognitive Systems, Technical University of Munich, Germany (Ramirez-Amaro, Karinne; Cheng, Gordon)
Abstract	In this paper, we present a two-stage framework that deal with the problem of automatically extract human activities from videos. First, for action recognition we employ an unsupervised state-of-the-art learning algorithm based on Independent Subspace Analysis (ISA). This learning algorithm extracts spatio-temporal features directly from video data and it is computationally more efficient and robust than other unsupervised methods. Nevertheless, when applying this one-stage state-of-the-art action recognition technique on the observations of human everyday activities, it can only reach an accuracy rate of approximately 25%. Hence, we propose to enhance this process with a second stage, which define a new method to automatically generate semantic rules that can reason about human activities. The obtained semantic rules enhance the human activity recognition by reducing the complexity of the perception system and they allow the possibility of domain change, which can great improve the synthesis of robot behaviors. The proposed method was evaluated under two complex and challenging scenarios: making a pancake and making a sandwich. The difficulty of these scenarios is that they contain finer and more complex activities than the well known data sets (Hollywood2, KTH, etc). The results show benefits of two stages method, the accuracy of action recognition was significantly improved compared to a single-stage method (above 87% compared to human expert). This indicates the improvement of the framework using the reasoning engine for the automatic extraction of human activities from observations, thus, providing a rich mechanism for transferring a wide range of human skills to humanoid robots.
Starting Page	456
Ending Page	461
File Size	1783354
Page Count	6
File Format	PDF
ISBN	9781479926176
ISSN	21640572
e-ISBN	9781479926190
DOI	10.1109/HUMANOIDS.2013.7030014
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Accuracy Feature extraction Cameras Videos Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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