(2020-10) Biagini, Martín; Filipic, Joaquín; Mas, Ignacio; Pose, Claudio D.; Giribet, Juan I.; Parisi, Daniel
"We propose the use of convolutional neural networks (CNN) for counting and positioning people in visible and infrared images. Our data set is made of semi-artificial images created from real photographs taken from a drone using a dual FLIR camera. We compare the performance between CNN’s using 3 (RGB) and 4 (RGB+IR) channels, both under different lighting conditions. The 4-channel network responds better in all situations, particularly in cases of poor visible illumination that can be found in night scenarios. The proposed methodology could be applied to real situations when an extensive databank of 4-channel images will be available."