This research shows a prototype for crowd location and counting for earthquakes based on deep learning and the infrastructure of a state-of-the-art 5G standalone network deployed at the Universidad de Concepcion, Chile. The system uses an 8 MP panoramic network camera to capture real-time crowd images, which are sent to a Deep Learning Server (DLS) over the 5G network. The camera provides visible color images, and its sensor technology can provide color images even at night. The DLS uses frames from the video feed and generates Focal Inverse Distance Transform (FIDT) maps, in which the counting and location of people are carried out. In particular, the FIDT maps are generated from the crowd images using a deep-learning model composed of two cascaded autoencoders. The 5G technology allows the system to transfer data from the camera to DLS at high speed, an essential feature for a system that will help authorities make critical decisions during natural disasters. Under this scenario, and considering that the number of rescuers is usually limited, our system enables a better distribution of them among several crowded places by instantly knowing the number of people at any time of the day or night.
In this paper a novel nonuniformity correction method that compensates for the fixed-pattern noise (FPN)
in infrared focal-plane array (IRFPA) sensors is developed. The proposed NUC method compensates for the
additive component of the FPN statistically processing the read-out signal using a noise-cancellation system.
The main assumption of the method is that a source of noise correlated to the additive noise of the IRFPA is
available to the system. Under this assumption, a finite impulse response (FIR) filter is designed to synthesize
an estimate of the additive noise. Moreover, exploiting the fact that the assumed source of noise is constant
in time, we derive a simple expression to calculate the estimate of the additive noise. Finally, the estimate
is subtracted to the raw IR imagery to obtain the corrected version of the images. The performance of the
proposed system and its ability to compensate for the FPN are tested with infrared images corrupted by both
real and simulated nonuniformity.
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