Robust abandoned object detection integrating wide area visual surveillance and social context

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust abandoned object detection integrating wide area visual surveillance and social context

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Robust abandoned object detection integrating wide area visual surveillance and social context

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/7E10045" target="_blank" >7E10045: Massive Sets of Heuristics for Machine Learning</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

789-798

Kód UT WoS článku

000317554500010

EID výsledku v databázi Scopus

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