Infrared dataset generation for people detection through superimposition of different camera sensors

Abstract

Infra-red (IR) cameras have found widespread use in many different fields. The most common ones are generally related to industrial applications, particularly maintenance and inspections activities. In the domain of surveillance, instead, they are mostly used for threat detection and security purposes. Pushed by cost reduction and the availability of compact sensors, intelligent IR cameras are gaining popularity in the field of Internet-of-Things, in light of the valuable information made available by the acquired data. Unfortunately, the achievable overall quality is not always satisfactory. For example, low-resolution devices, noise, or harsh environmental conditions, like high temperatures on sunny days, can degrade the quality of the thermal images. This paper presents the development of a portable, low-cost, and low-power thermal scanner prototype consisting of a thermal sensor assisted by a grayscale camera. The prototype is completely made using COTS components and provides 80 × 60 IR and 160 × 120 grayscale images, mostly used to collect and validate the IR-based data. Our application focuses on people detection, for which we present a suitable learning framework together with the corresponding IR dataset, collected and annotated via the paired grayscale images.