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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-61499-799-3

ISSN

0922-6389

e-ISSN

neuvedeno

Number of pages

12

Pages from-to

385-396

Publisher name

IOS press

Place of publication

Amsterdam

Event location

Kitakyushu; Japan

Event date

Sep 26, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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