Vision-Based Nighttime Vehicle Detection Using CenSurE and SVM

Abstract

In this paper, we propose a method for detecting vehicles from a nighttime driving scene taken by an in-vehicle monocular camera. Since it is difficult to recognize the shape of the vehicles during nighttime, vehicle detection is based on the headlights and the taillights, which are bright areas of pixels called blobs. Many research studies using automatic multilevel thresholding are being conducted, but these methods are prone to get affected by the ambient light because it uses the luminance of the whole image to derive the thresholds. Owing to such reasons, we focused on the Laplacian of Gaussian operator, which derives the response of luminance difference between the blob and its surroundings. Compared with automatic multilevel thresholding, Laplacian of Gaussian operator is more robust to the ambient light. However, the computational cost to derive the response of this operator is large. Therefore, we used a method called Center Surround Extremas to detect the blobs in high speed. Since the detected blobs include nuisance lights, we had to determine whether the blob is a light of a vehicle or not. Thus, we classified them according to the features of the blob using support vector machines. Then, we detected vehicle traffic lane and specified the region where the vehicle may exist. Finally, we classified the blobs based on the movements across the frames. We applied the proposed method to nighttime driving sequences and confirmed the effectiveness of the classification process used in this method and that it could process within frame rate.

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

“…For obtaining ROIs of vehicles, the following techniques can be adopted: threshold-based segmentation methods [5], [12], [18], paired vehicle lighting-based methods [6]- [8], [14]- [16], saliency map-based methods [17], [27], and artificially designed feature-based methods [13]. After the ROIs are obtained, we need to further determine whether these candidate regions contain vehicles.…”

“…Chen et al [27] generated ROIs based on the saliency method and applied the deformable parts model (DPM) to detect vehicles. Kosaka et al [13] used SVM to classify vehicles after using Laplacian of Gaussian operation to detect the blobs.…”

Shadow-Based Vehicle Detection in Urban Traffic

“…However, these methods are very sensitive to illumination changes and specular reflections that may cause the loss of true positives. Noting that taillights are an important feature for vehicle detection at night time, in [15,16] vehicle hypotheses are generated using a morphological filter to detect the taillight pair in a narrow horizontal search region. However, this approach is only applicable for night time vehicle detection.…”

Deep Dual-Modal Traffic Objects Instance Segmentation Method Using Camera and LIDAR Data for Autonomous Driving

“…Silberman and Fergus presented an approach for indoor semantic segmentation by interpreting the major surfaces, targets, and support relations of an indoor scene from an RGB-D image [7]. A method for detecting a vehicle in a night driving scene taken from a vehicle monocular camera has been proposed by using a method called Center Surround Extremas to detect the blobs at high speed, based on the Laplacian of the Gaussian operator [8]. Cheon et al proposed a vision-based We tested Mask R-CNN [3], Retina-Mask [4], and YOLACT [5], which are state-of-the-art instance segmentation networks, on our own collected dataset under various environmental conditions (sunny day-time, rainy, smoggy, and night-time).…”