Fusing concurrent visible and infrared videos for improved tracking performance

Abstract

Visible and infrared video cameras are the most common imaging sensors used for video surveillance systems. Fusing concurrent visible and infrared imageries may further improve the overall detection and tracking performance of a video surveillance system. We performed image fusion using 13 pixel-based image fusion algorithms and examined their effects on the detection and tracking performance of a given target tracker. We identified five fusion methods that produced significantly better performance, three of which also managed to achieve that with a relatively high efficiency.