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1. VISION-BASED NIGHTTIME VEHICLE DETECTION USING CENSURE AND SVM
ABSTRACT
In this paper, we propose a method for detectingvehicles from a nighttime driving scene
taken by an in-vehiclemonocular camera. Since it is difficult to recognize the shape ofthe
vehicles during nighttime, vehicle detection is based on theheadlights and the taillights, which
are bright areas of pixelscalled blobs. Many research studies using automatic multi level th
resoling are being conducted, but these methods are prone toget affected by the ambient light
because it uses the luminance ofthe whole image to derive the thresholds. Owing to such
reasons,we focused on the Laplacian of Gaussian operator, which derivesthe response of
luminance difference between the blob and itssurroundings. Compared with automatic multilevel
thre sholding, Laplacian of Gaussian operator is more robust to the ambientlight. However, the
computational cost to derive the responseof this operator is large. Therefore, we used a method
calledCenter Surround Extremas to detect the blobs in high speed. Sincethe detected blobs
include nuisance lights, we had to determinewhether the blob is a light of a vehicle or not. Thus,
we classifiedthem according to the features of the blob using support vectormachines. Then, we
detected vehicle traffic lane and specifiedthe region where the vehicle may exist. Finally, we
classified theblobs based on the movements across the frames. We applied theproposed method
to nighttime driving sequences and confirmedthe effectiveness of the classification process used
in this methodand that it could process within frame rate.