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. The experiments conducted prove effectiveness of the proposed solution.
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