Vision-Based Nighttime Vehicle Detection Using CenSurE and SVM


In this paper, we have a tendency to propose a method for detecting vehicles from a nighttime driving scene taken by an in-vehicle monocular camera. Since it's troublesome to acknowledge the form of the vehicles throughout nighttime, vehicle detection is predicated on the headlights and also the taillights, that are bright areas of pixels known as blobs. Many research studies using automatic multilevel thresholding are being conducted, but these methods are prone to get plagued by the ambient light as a result of it uses the luminance of the entire image to derive the thresholds. Owing to such reasons, we focused on the Laplacian of Gaussian operator, that derives the response of luminance difference between the blob and its surroundings. Compared with automatic multilevel thresholding, Laplacian of Gaussian operator is more sturdy to the ambient light. However, the computational cost to derive the response of this operator is giant. Therefore, we have a tendency to used a method called Center Surround Extremas to detect the blobs in high speed. Since the detected blobs embrace nuisance lights, we tend to had to determine whether or not the blob could be a lightweight of a vehicle or not. So, we tend to classified them per the options of the blob using support vector machines. Then, we have a tendency to detected vehicle traffic lane and specified the region where the vehicle could exist. Finally, we classified the blobs based mostly on the movements across the frames. We applied the proposed method to nighttime driving sequences and confirmed the effectiveness of the classification process utilized in this method and that it might process within frame rate.

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