DocumentCode
3089137
Title
An ontology framework for automated visual surveillance system
Author
Sobhani, Faranak ; Kahar, Nur Farhan ; Qianni Zhang
Author_Institution
Multimedia Vision Res. Group Sch. of Electron. Eng. & Comput. Sci., Queen Mary, Univ. of London, London, UK
fYear
2015
fDate
10-12 June 2015
Firstpage
1
Lastpage
7
Abstract
This paper presents analysis and development of a forensic domain ontology to support an automated visual surveillance system. The proposed domain ontology is built on a specific use case based on the severe riots that swept across major UK cities with devastating effects during the summer 2011. The proposed ontology aims at facilitating the description of activities, entities, relationships, resources and consequences of the event. The study exploits 3.07 TB data provided by the Londons Metropolitan Police (Scotland Yard) as a part of European LASIE project1. The data has been analyzed and used to guarantee adherence to a real-world application scenario. A `top-down development´ approach to the ontology design has been taken. The ontology is also used to demonstrate how high level reasoning can be incorporated into an automatop-ted forensic system. Thus, the designed ontology is also the base for future development of knowledge inference as response to domain specific queries.
Keywords
digital forensics; ontologies (artificial intelligence); video surveillance; European LASIE project; Londons Metropolitan Police; automated visual surveillance system; automatop-ted forensic system; domain specific query; forensic domain ontology; high level reasoning; knowledge inference; ontology design; ontology framework; top-down development approach; Cognition; Forensics; Ontologies; Semantic Web; Semantics; Surveillance; Visualization; forensic computing; ontology; visual surveillance;
fLanguage
English
Publisher
ieee
Conference_Titel
Content-Based Multimedia Indexing (CBMI), 2015 13th International Workshop on
Conference_Location
Prague
Type
conf
DOI
10.1109/CBMI.2015.7153628
Filename
7153628
Link To Document