  Fire is considered one of the most serious threats to human lives which
results in a high probability of fatalities. Those severe consequences stem
from the heavy smoke emitted from a fire that mostly restricts the visibility
of escaping victims and rescuing squad. In such hazardous circumstances, the
use of a vision-based human detection system is able to improve the ability to
save more lives. To this end, a thermal and infrared imaging fusion strategy
based on multiple cameras for human detection in low-visibility scenarios
caused by smoke is proposed in this paper. By processing with multiple cameras,
vital information can be gathered to generate more useful features for human
detection. Firstly, the cameras are calibrated using a Light Heating
Chessboard. Afterward, the features extracted from the input images are merged
prior to being passed through a lightweight deep neural network to perform the
human detection task. The experiments conducted on an NVIDIA Jetson Nano
computer demonstrated that the proposed method can process with reasonable
speed and can achieve favorable performance with a<EMAIL_ADDRESS>of 95%.
