Stampede prediction based on individual activity recognition for context-aware framework sing sensor-fusion in a crowd scenarios

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013619" target="_blank" >RIV/62690094:18450/17:50013619 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-385" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-800-6-385</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-385" target="_blank" >10.3233/978-1-61499-800-6-385</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stampede prediction based on individual activity recognition for context-aware framework sing sensor-fusion in a crowd scenarios

Popis výsledku v původním jazyce

With the benefits of context-aware and the smartphone&apos;s participatory sensing potential, individual activity recognition (IAR) has proven to be enormously importance for stampede prediction in a crowd using a geographical location and global positioning system (GPS) data. In the case of an unforeseen incident and in an emergency situations whether in a small or large gathering. The research effort used Kalman filter to remove uncertainty through sensor fusion to create room for a reliable measurement for abnormality prediction. This paper, addressed the following questions. (i) How to determine the flow direction and the velocity of peoples&apos; movement in a crowd to know when stampede will occur? (ii) What is the role of sensor fusion in a crowd scenario? Two scenarios experimented on IAR with accelerometer, GPS, and digital compass sensors to determine the flow pattern of participants&apos; movement in a crowd using the flow velocity Vsi and flow direction Dsi, in the proposed stampede prediction approach. The experimental results show the effect of Vsi and Dsi for different group locations and serve as a pointer to reduce risk towards mitigation of crowd disaster and enhanced the existing context-aware framework to save human lives in our society if used in crowd scenarios.

Název v anglickém jazyce

Stampede prediction based on individual activity recognition for context-aware framework sing sensor-fusion in a crowd scenarios

Popis výsledku anglicky

With the benefits of context-aware and the smartphone&apos;s participatory sensing potential, individual activity recognition (IAR) has proven to be enormously importance for stampede prediction in a crowd using a geographical location and global positioning system (GPS) data. In the case of an unforeseen incident and in an emergency situations whether in a small or large gathering. The research effort used Kalman filter to remove uncertainty through sensor fusion to create room for a reliable measurement for abnormality prediction. This paper, addressed the following questions. (i) How to determine the flow direction and the velocity of peoples&apos; movement in a crowd to know when stampede will occur? (ii) What is the role of sensor fusion in a crowd scenario? Two scenarios experimented on IAR with accelerometer, GPS, and digital compass sensors to determine the flow pattern of participants&apos; movement in a crowd using the flow velocity Vsi and flow direction Dsi, in the proposed stampede prediction approach. The experimental results show the effect of Vsi and Dsi for different group locations and serve as a pointer to reduce risk towards mitigation of crowd disaster and enhanced the existing context-aware framework to save human lives in our society if used in crowd scenarios.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-61499-799-3

ISSN

0922-6389

e-ISSN

neuvedeno

Počet stran výsledku

12

Strana od-do

385-396

Název nakladatele

IOS press

Místo vydání

Amsterdam

Místo konání akce

Kitakyushu; Japan

Datum konání akce

26. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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