SPIE Journal Paper | 28 June 2018
KEYWORDS: Feature extraction, Feature selection, Image enhancement, Taillights, RGB color model, Headlamps, Intelligence systems, Navigation systems, Image segmentation, Video
Nighttime vehicle detection is part of an intelligent transportation system for road safety, driving navigation, and surveillance at night. However, previous nighttime vehicle detection methods only deal with a single class or two classes of vehicles. This paper presents an effective system based on cascade feature selection and a coarse-to-fine mechanism for detecting preceding multiclass vehicles at night. First, we train a coarse-level classifier using contrast features. Second, we combine a cascade selection framework with feature mutual correlation, similarity and regions’ overlap to select a set of image regions, where we can extract effective features. Three features, including local binary pattern, histogram of oriented gradients, and four direction features, are extracted from the selected regions for training a fine-level multiclass vehicle classifier. During the detection stage, we utilize a coarse-to-fine mechanism. In the coarse level, a multiscale sliding window is classified by the coarse-level classifier to find regions of interest (ROIs) that are likely to be vehicles. In the fine level, these ROIs are identified by the trained multiclass vehicle classifier. Evaluations on a Hong Kong nighttime multiclass vehicle dataset show that our proposed system successfully detects a car, taxi, bus, and minibus within nighttime images under different scenes. Quantitatively, our proposed system obtains 95.48% detection rate at 0.055 false positives per image, outperforming some state-of-the-art detection approaches including two current nighttime vehicle detection methods, and two deep learning-based methods.
