Abstract
The automatic detection of human activities requires large computational resources to increase recognition performances and sophisticated capturing devices to produce accurate results. Anyway, often innovative analysis methods applied to data extracted by off-the-shelf detection peripherals can return acceptable outcomes. In this paper a framework is proposed for automated posture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A simple yet general data model and a corresponding ontology create the needed terminological substratum for an automatic posture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking allows to compare retrieved annotations with standard posture descriptions stored as individuals in a proper Knowledge Base. Finally, non-standard inferences and a similarity-based ranking support the discovery of the best matching posture. This framework has been implemented in a prototypical tool and preliminary experimental tests have been carried out w.r.t. a reference dataset.
The authors wish to acknowledge support from National Operative Program project Res Novae (Grid, Building and Road Objectives for Environment and Energy).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Colucci, S., Di Noia, T., Pinto, A., Ragone, A., Ruta, M., Tinelli, E.: A Non-Monotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces. International Journal of Electronic Commerce 12(2), 127–154 (2007)
Baader, F., Calvanese, D., Mc Guinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook. Cambridge University Press (2002)
Fothergill, S., Mentis, H., Kohli, P., Nowozin, S.: Instructing people for training gestural interactive systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1737–1746. ACM, New York (2012)
Zhang, T., Xu, C., Zhu, G., Liu, S., Lu, H.: A generic framework for video annotation via semi-supervised learning. IEEE Transactions on Multimedia 14(4), 1206–1219 (August)
Saad, S., De Beul, D., Mahmoudi, S., Manneback, P.: An ontology for video human movement representation based on benesh notation. In: 2012 International Conference on Multimedia Computing and Systems (ICMCS), pp. 77–82. IEEE (2012)
François, A.R.J., Nevatia, R., Hobbs, J., Bolles, R.C., Smith, J.R.: VERL: an ontology framework for representing and annotating video events. IEEE MultiMedia 12(4), 76–86 (2005)
Vrusias, B., Makris, D., Renno, J.-P., Newbold, N., Ahmad, K., Jones, G.: A framework for ontology enriched semantic annotation of CCTV video. In: Eighth International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2007, p. 5. IEEE (2007)
Akdemir, U., Turaga, P., Chellappa, R.: An ontology based approach for activity recognition from video. In: Proceedings of the 16th ACM International Conference on Multimedia, MM 2008, pp. 709–712. ACM, New York (2008)
SanMiguel, J.C., Martinez, J.M., Garcia, A.: An ontology for event detection and its application in surveillance video. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 220–225. IEEE (2009)
Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems 5(4), 410–430 (2009)
Gómez-Romero, J., Patricio, M.A., GarcÃa, J., Molina, J.M.: Ontology-based context representation and reasoning for object tracking and scene interpretation in video. Expert Syst. Appl. 38(6), 7494–7510 (2011)
Miranda, L., Vieira, T., Martinez, D., Lewiner, T.: Real-time gesture recognition from depth data through key poses learning and decision forests. In: Sibgrapi 2012 (XXV Conference on Graphics, Patterns and Images), Ouro Preto, MG. IEEE (August 2012)
Raptis, M., Kirovski, D., Hoppe, H.: Real-time classification of dance gestures from skeleton animation. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 147–156. ACM, New York (2011)
Renz, J., Mitra, D.: Qualitative direction calculi with arbitrary granularity. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 65–74. Springer, Heidelberg (2004)
Reis, B., Teixeira, J.M., Breyer, F., Vasconcelos, L.A., Cavalcanti, A., Ferreira, A., Kelner, J.: Increasing Kinect application development productivity by an enhanced hardware abstraction. In: 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 5–14. ACM, New York (2012)
Kato, J., McDirmid, S., Cao, X.: Dejavu: Integrated support for developing interactive camera-based programs. In: 25th Annual ACM Symposium on User Interface Software and Technology, pp. 189–196. ACM, New York (2012)
Ruta, M., Scioscia, F., Di Sciascio, E., Gramegna, F., Loseto, G.: Mini-ME: the Mini Matchmaking Engine. In: Horrocks, I., Yatskevich, M., Jimenez-Ruiz, E. (eds.) OWL Reasoner Evaluation Workshop (ORE 2012). CEUR Workshop Proceedings, vol. 858, pp. 52–63. CEUR-WS (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ruta, M., Scioscia, F., di Summa, M., Ieva, S., Di Sciascio, E., Sacco, M. (2014). Body Posture Recognition as a Discovery Problem: A Semantic-Based Framework. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-09912-5_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09911-8
Online ISBN: 978-3-319-09912-5
eBook Packages: Computer ScienceComputer Science (R0)