In-image rain wiper elimination for vision-based Advanced Driver Assistance Systems

Abstract

This paper proposes a new algorithm to eliminate the wiper interference in a vehicle's onboard video to improve the detection rate of vision-based Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW) Systems. During raining days, the windshield wipers periodically and partially block the appearance of obstacles on the road that are to be detected in these early warning systems, and hence, impair their performance. The wiper pixels could be in-painted via (1) localizing the pixels belonging to the wipers and (2) replacing the wiper pixels with non-wiper pixels extracted from an adjacent video frame with no such blockage. Finally, since it is impossible to obtain the images captured in the same scenario with and without wiper interference, a quantitatively analyzing framework which blends wipers into non-wiper images is also proposed to quantitatively assess the detection rate of data with wipers, without wipers and with wipers but eliminated by our algorithm. The experimental results show that the wiper pixels being segmented and in-painted blend in unobtrusively with the surrounding pixels and the FCW detection rate is indeed improved.