Robust abandoned object detection integrating wide area visual surveillance and social context
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00205806" target="_blank" >RIV/68407700:21230/13:00205806 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.patrec.2013.01.018" target="_blank" >http://dx.doi.org/10.1016/j.patrec.2013.01.018</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patrec.2013.01.018" target="_blank" >10.1016/j.patrec.2013.01.018</a>
Alternative languages
Result language
angličtina
Original language name
Robust abandoned object detection integrating wide area visual surveillance and social context
Original language description
This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of theOwner
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/7E10045" target="_blank" >7E10045: Massive Sets of Heuristics for Machine Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Pattern Recognition Letters
ISSN
0167-8655
e-ISSN
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Volume of the periodical
34
Issue of the periodical within the volume
7
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
10
Pages from-to
789-798
UT code for WoS article
000317554500010
EID of the result in the Scopus database
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