TY - GEN
T1 - Object localization in handheld thermal images for fireground understanding
AU - Vandecasteele, Florian
AU - Merci, Bart
AU - Jalalvand, Azarakhsh
AU - Verstockt, Steven
N1 - Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - Despite the broad application of the handheld thermal imaging cameras in firefighting, its usage is mostly limited to subjective interpretation by the person carrying the device. As remedies to overcome this limitation, object localization and classification mechanisms could assist the fireground understanding and help with the automated localization, characterization and spatio-temporal (spreading) analysis of the fire. An automated understanding of thermal images can enrich the conventional knowledge-based firefighting techniques by providing the information from the data and sensing-driven approaches. In this work, transfer learning is applied on multi-labeling convolutional neural network architectures for object localization and recognition in monocular visual, infrared and multispectral dynamic images. Furthermore, the possibility of analyzing fire scene images is studied and their current limitations are discussed. Finally, the understanding of the room configuration (i.e., objects location) for indoor localization in reduced visibility environments and the linking with Building Information Models (BIM) are investigated.
AB - Despite the broad application of the handheld thermal imaging cameras in firefighting, its usage is mostly limited to subjective interpretation by the person carrying the device. As remedies to overcome this limitation, object localization and classification mechanisms could assist the fireground understanding and help with the automated localization, characterization and spatio-temporal (spreading) analysis of the fire. An automated understanding of thermal images can enrich the conventional knowledge-based firefighting techniques by providing the information from the data and sensing-driven approaches. In this work, transfer learning is applied on multi-labeling convolutional neural network architectures for object localization and recognition in monocular visual, infrared and multispectral dynamic images. Furthermore, the possibility of analyzing fire scene images is studied and their current limitations are discussed. Finally, the understanding of the room configuration (i.e., objects location) for indoor localization in reduced visibility environments and the linking with Building Information Models (BIM) are investigated.
KW - convolutional neural networks
KW - object detection and recognition
KW - Thermal fire analysis
UR - http://www.scopus.com/inward/record.url?scp=85023613182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023613182&partnerID=8YFLogxK
U2 - 10.1117/12.2262484
DO - 10.1117/12.2262484
M3 - Conference contribution
AN - SCOPUS:85023613182
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Thermosense
A2 - Burleigh, Douglas
A2 - Bison, Paolo
PB - SPIE
T2 - Thermosense: Thermal Infrared Applications XXXIX 2017
Y2 - 10 April 2017 through 13 April 2017
ER -