A Flexible and Scalable Cooperative Surveillance Technique Using Integrated Ontology Reasoning

Abstract:

In large-scale smart camera networks, cooperation among devices is required for continuous tracking of targets and higher level reasoning. A large amount of multimedia data with derived metadata is generated and transferred among devices. In this paper, we design a large-scale surveillance system which consists of smart cameras. It complies with the standard specification to ensure interoperability among cameras and flexibility regarding integration of new devices and services. Surveillance data contained in them is integrated and structured according to the ontology, and useful context information can be derived. This paper introduces how to build surveillance knowledge base, import relevant data from other devices, and annotate data on interoperable framework which accommodates to the standard. The annotation process provides an impetus to the improvement of knowledge over time. We define a representative reasoning architecture that provides location-based context induction, and implemented in our test bed site to show superiority in large-scale surveillance.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3246-3250
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3246
• Online since: January 2013
• Authors: Pil Seong Park, Soo Mi Yang
• Keywords: Large-Scale Surveillance System, Ontology Reasoning, Smart Camera Networks, Web Service

References

[1] ONVIF (Open Network Video Interface Forum) http: /www. onvif. org.

[2] A. Schlicht, H. Stuckenschmidt, Towards Distributed Ontology Reasoning for the Web, WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008) 536-539.

DOI: 10.1109/wiiat.2008.396

[3] T. R. Gopalakrishnan Nair, P. Jayarekha, A Rank Based Replacement Policy for Multimedia Server Cache Using Zipf-Like Law, Journal of Computing, Volume 2, Issue 3 (2010) 14-22.

[4] PSIA Specification Package Q12009, http: /www. psiaalliance. org.

[5] J. Gomez-Romero, M. A. Patricio, J. Garcia, J. M. Molina, in: Communication in distributed tracking system: an ontology-based approach to improve cooperation, Expert Systems-The Journal of Knowledge Engineering, Vol. 28, No. 4 (2011) 288-305.

DOI: 10.1111/j.1468-0394.2011.00600.x

[6] K. Chitra, Dr. G. Padmavathi, FAVQCHOKE: To Allocate Fair Buffer to a Dynamically Varying Traffic in an IP Network, International Journal of Distributed and Parallel Systems Vol. 2, No. 1 (2011) 73-82.

DOI: 10.5121/ijdps.2011.2106

[7] T. Gruber, A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, Vol. 5 (1993) 199-220.

DOI: 10.1006/knac.1993.1008

[8] R. Neches, R. Fikes, T. Finin, T. Gruber, R. Ratil, T. Senator, W. R. Swartout, Enabling Technology for Knowledge Sharing, AI Magazine. Winter (1991) 36-56.

[9] S. Yang, H. Kumar, P. Park, An Expert System for Wide Area Surveillance Based on Ontology, International Journal of Database Theory and Application, Vol. 5 (2012) 1-15.