research-article

Semantic analysis of human movements in videos

Authors:

Saïd Mahmoudi,

Pierre MannebackAuthors Info & Claims

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

Pages 141 - 148

https://doi.org/10.1145/2362499.2362519

Published: 05 September 2012 Publication History

Abstract

Segmentation and representation of movements in videos play an important role in different applications such as search engines, video recommender systems, and video summarizers. In this paper, we present a system for semantic annotation of movements in video. This system is based on the temporal segmentation method that extracts the movement objects in still scenes, and on the high-level movement concepts to bridge the semantic gap between such concepts and the low-level video features. We propose a knowledge-based Model of movements in videos by using the OWL ontology and SWRL rules. Our Video Movement Ontology (VMO) considers different concepts related to the relevant movement features, which is based on the semantic of the Benesh Movement Notation (BMN). BMN can describe any form of dance or human movement. Rules in description logic are defined to describe how low-level features and mapping process between those features and ontology's concepts should be applied according to different perception of video content analysis. This system can improve the quality of annotation of movements in the videos and can discover the hidden information by reasoning video knowledge and movement's features.

References

[1]

Pellet owl 2 reasoner for java: http://clarkparsia.com/pellet.

[2]

Reasoner hermit owl reasoner: http://hermit-reasoner.com.

[3]

Ansgar, S., and Susanne, B. Managing Multimedia Semantics. Idea Group Publishing, 2005, ch. MM4U - A framework for creating personalized multimedia content.

[4]

Arnold, S., Marcel, W., Simone, S., Amarnath, G., and Ramesh, J. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000), 1349--1380.

Digital Library

[5]

Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., and Patel-Schneider, P. F., Eds. The Description Logic Handbook: Theory, Implementation, and Applications (2003), Cambridge University Press.

Digital Library

[6]

Benesh, R., and Benesh, J. Reading dance: The birth of Choreography, first ed. Souvenir Press LTD, 1983.

[7]

Chen, L., Nugent, C., Biswas, J., and Hoey, J. An ontology-based context-aware approach for behaviour analysis. In Activity Recognition in Pervasive Intelligent Environments, vol. 4. Atlantis Press, Paris, France, 2011, pp. 127--148.

[8]

Gruber, T. R. A translation approach to portable ontology specifications. Knowl. Acquis. 5, 2 (1993), 199--220.

Digital Library

[9]

Hollink, L., Little, S., and Hunter, J. Evaluating the application of semantic inferencing rules to image annotation. In Proceedings of the 3rd international conference on Knowledge capture (New York, NY, USA, 2005), K-CAP "05, ACM, pp. 91--98.

Digital Library

[10]

Kokaram, A., Rea, N., Dahyot, R., Tekalp, M., Bouthemy, P., Gros, P., and Sezan, I. Browsing sports video: trends in sports-related indexing and retrieval work. IEEE Signal Processing Magazine (2006).

[11]

Nakamura, M., and Hachimura, K. An xml representation of labanotation, labanxml, and its implementation on the notation editor labaneditor2. In In first South-Eastern European Digitization Initiative (SEEDI) Conference (2005).

[12]

Neagle, R. J., Ng, K., and Ruddle, R. A. Developing a virtual ballet dancer to visualise choreography. In AISB 2004 on Language, Speech Gesture for Expressive Characters (AISB'04) (2004), pp. 86--97.

[13]

Noy, N., and McGuinness, D. L. Ontology development 101: A guide to creating your first ontology. Tech. rep., Stanford Knowledge Systems Laboratory and Stanford Medical Informatics, 2001.

[14]

Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 1 (1986), 81--106.

Digital Library

[15]

Raheb, K. E., and Ioannidis, Y. A labanotation based ontology for representing dance movement. in Proceedings of Gesture Workshop (2011).

[16]

Saad, S., Beul, D., Mahmoudi, S., and Manneback, P. An ontology for video human movement representation based on benesh notation. in Proceedings IEEE of The 3rd International Conference on Multimedia Computing and Systems (ICMCS'12), Tangier, Morocco (May 10--12 2012,).

[17]

SanMiguel, Carlos, J., Martinez, M., J., and Álvaro, G. An ontology for event detection and its application in surveillance video. In Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (Washington, DC, USA, 2009), AVSS "09, IEEE Computer Society, pp. 220--225.

Digital Library

[18]

Snoek, C., and Worring, M. Multimodal video indexing: A review of the state-of-the-art. Multimedia Tools and Applications 25 (2003), 5--35.

Digital Library

[19]

Zhang, Y. J. An overview of image and video segmentation in the last 40 years. Advances in Image and Video Segmentation (2006), 1--15.

Cited By

Ibrahim EZaghden NMejdoub M(2024)Semantic Analysis System to Recognize Moving Objects by Using a Deep Learning ModelIEEE Access10.1109/ACCESS.2024.341089412(80740-80753)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3410894
Ibrahim EMejdoub MZaghden N(2022)Semantic Analysis of Moving Objects in Video SequencesProceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_25(257-269)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_25
Kanak A(2018)Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use caseIET Biometrics10.1049/iet-bmt.2018.50677:6(510-518)Online publication date: 10-Oct-2018
https://doi.org/10.1049/iet-bmt.2018.5067
Show More Cited By

Semantic analysis of human movements in videos
1. Computing methodologies
  1. Artificial intelligence

Recommendations

Using the relation ontology Metarel for modelling Linked Data as multi-digraphs
Linked Data for Health Care and the Life Sciences

The Semantic Web standards OWL and RDF are often used to represent biomedical information as Linked Data; however, the OWL/RDF syntax, which combines both, was never optimised for querying. By combining two formal paradigms for modelling Linked Data, ...
RDF, Jena, SparQL and the 'Semantic Web'
SIGUCCS '09: Proceedings of the 37th annual ACM SIGUCCS fall conference: communication and collaboration

The Resource Description Format (RDF) is used to represent information modeled as a "graph": a set of individual objects, along with a set of connections among those objects. In that role, RDF is one of the pillars of the so-called Semantic Web. This ...
A SPARQL Query Rewriting Approach on Heterogeneous Ontologies with Mapping Reliability
IIAI-AAI '12: Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics

SPARQL is a standard query language for RDF data that are commonly used to represent and store Semantic Web data. There are a lot of SPARQL endpoints to retrieve and see the data by SPARQL queries. Although it greatly helps us query semantic data with ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

215 pages

ISBN:9781450311120

DOI:10.1145/2362499

Editors:
Harald Sack
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH
,
Tassilo Pellegrini
University of Applied Sciences St. Pölten
,
Program Chairs:
Valentina Presutti
Institute for Cognitive Science and Technology, Rome - Italy
,
H. Sofia Pinto
Technical University of Lisbon - Portugal

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

I-SEMANTICS '12

I-SEMANTICS '12: I-SEMANTICS 2012 - 8th International Conference on Semantic Systems

September 5 - 7, 2012

Graz, Austria

Acceptance Rates

Overall Acceptance Rate 40 of 182 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
201
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ibrahim EZaghden NMejdoub M(2024)Semantic Analysis System to Recognize Moving Objects by Using a Deep Learning ModelIEEE Access10.1109/ACCESS.2024.341089412(80740-80753)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3410894
Ibrahim EMejdoub MZaghden N(2022)Semantic Analysis of Moving Objects in Video SequencesProceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_25(257-269)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_25
Kanak A(2018)Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use caseIET Biometrics10.1049/iet-bmt.2018.50677:6(510-518)Online publication date: 10-Oct-2018
https://doi.org/10.1049/iet-bmt.2018.5067
Luca RBejinariu STeodorescu H(2014)An ontology of human walk for autonomous systems2014 18th International Conference on System Theory, Control and Computing (ICSTCC)10.1109/ICSTCC.2014.6982464(488-493)Online publication date: Oct-2014
https://doi.org/10.1109/ICSTCC.2014.6982464

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents