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Night vision pedestrian detection based on adaptive preprocessing using near infrared camera

Performance of computer vision algorithm for pedestrian detection decrease in case of night vision system. Since intensity of luminance at night is extremely low compare to daytime, even human visibility cannot recognize object properly at night. Most recent generation of night vision system have researched using near infrared camera to settle the problem, but it still have poor visibility and application. This paper describes a night vision pedestrian detection for AEB (Automatic Emergency Braking) system using near infrared camera. ACF (Aggregated Channel Features) and AdaBoost algorithm are employed to discriminate pedestrians reflected by infrared. Most significant contribution of this paper is adaptive preprocessing to enhance contrast of pedestrians. Implementation have been done on both desktop and embedded board. With improved pedestrian detection performance, our approach allow drivers to avoid potential collisions effectively.