TY - GEN
T1 - A decentralised disaster detection approach using image data
AU - Talat, Romana
AU - Muzammal, Muhammad
AU - Gohar, Moneeb
AU - Rahman, Arif Ur
AU - Pirbhulal, Sandeep
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Flood disaster mostly happens due to instant heavy rain fall or sudden increase in water level in rivers. Such natural disaster may results in excessive loss of human life and property. However, it is very important to aware the residential areas before and during the disaster by disseminating information. With the rapid development of devices embedded with internetof- things (IoT), this may bring a lot of benefits to propagate the information among people. In this work, we proposed a decentralized disaster detection approach using image data. The proposed framework consist of set of sensor devices capable of capturing the images. Each device is able to process the image and generate warning alarm based on its decision. For detection of flood, we applied thresholding-based segmentation and morphological operations. We performed extensive experiments to validate our proposed approach. For analysis purpose, we considered images taken from different distance and our proposed approach provides promising results.
AB - Flood disaster mostly happens due to instant heavy rain fall or sudden increase in water level in rivers. Such natural disaster may results in excessive loss of human life and property. However, it is very important to aware the residential areas before and during the disaster by disseminating information. With the rapid development of devices embedded with internetof- things (IoT), this may bring a lot of benefits to propagate the information among people. In this work, we proposed a decentralized disaster detection approach using image data. The proposed framework consist of set of sensor devices capable of capturing the images. Each device is able to process the image and generate warning alarm based on its decision. For detection of flood, we applied thresholding-based segmentation and morphological operations. We performed extensive experiments to validate our proposed approach. For analysis purpose, we considered images taken from different distance and our proposed approach provides promising results.
UR - http://www.scopus.com/inward/record.url?scp=85069002197&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2019.8746674
DO - 10.1109/VTCSpring.2019.8746674
M3 - Conference contribution
AN - SCOPUS:85069002197
T3 - IEEE Vehicular Technology Conference
BT - 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 89th IEEE Vehicular Technology Conference, VTC Spring 2019
Y2 - 28 April 2019 through 1 May 2019
ER -