Embedded Night-Vision System For Pedestrian Detection

Abstract

Assistive vision-based solutions for the driver extend the capabilities of human vision and support safe travel.
Unfortunately, their widespread usage is generally limited to expensive cars. Interestingly, a high price is most
likely a derivative of the costs incurred in the research instead of the value of hardware components. In the article
we show that a mobile system for pedestrian detection in severe lighting conditions can be build using state of the
art algorithms and widely available hardware. The proposed night-vision system for pedestrian detection
processes thermal images using a proprietary ODROID XU4 microcomputer under Ubuntu MATE operating
system. We applied a cascade object detector for the task of human silhouette detection in context of thermal
imagery and contrasted the results with the state of the art deep learning approach.