LAUSR

Abstract Achieving clear imaging through fire is a highly pursued goal and various active field-portable devices have been recently proposed to improve the capabilities of existing thermographic cameras. Here we combine an Infrared active imaging sensor and artificial intelligence to obtain automatic detection of people hidden behind flames. We show the successful use of a pre-trained Convolutional Neural Network in recognizing a static or moving person through fire when this is imaged by the proposed system. Remarkably, the network is able to detect the person even in the case the imaging system we propose cannot reject the flame disturbance in full, thus improving its robustness. These results pave the way to the development of automatic surveillance systems able to generate alerts in the case a fire spreads and persons are detected inside rooms invaded by flames, without relying on the subjective human interpretation of the videos.