Vehicle lamps are an important image feature of the night-time vehicle detection algorithm. This study proposes a real-time night-time vehicle detection algorithm based on light attenuation characteristic analysis, which consists of vehicle lamp detection and pairing. For the detection phase, this study proposes an automatic dual-threshold method to quickly extract the attenuation regions around bright objects. This method is highly adaptable and can accurately extract attenuation regions to identify vehicle lamps and strong reflections. For the pairing phase, this study uses the moving trajectories of vehicle lamps to complete preliminary pairing. Then, the vehicle lamp pairing is optimised using the relative vertical and horizontal lamp positions in continuous images. Pixel- and object-based methods were used to verify the experimental vehicle lamp detection results. The results of both verification methods indicate that the proposed algorithm without morphological operation is superior to other algorithms. The accuracy of the pairing phase was >94.6% in scenes with multiple lamps, high-driving speed, high traffic flow, and rain. Finally, the proposed night-time vehicle detection algorithm can achieve a real-time execution speed for images 960 × 720 pixels in size.
High-accuracy vehicle lamp detection for real-time night-time traffic surveillance
IET Intelligent Transport Systems ; 14 , 13 ; 1923-1934
2021-02-03
12 pages
Article (Journal)
Electronic Resource
English
object detection , road vehicles , road safety , edge detection , detection phase , pairing phase , vehicle lamp pairing , multiple lamps , experimental vehicle lamp detection results , real-time night-time traffic surveillance , horizontal lamp positions , high-accuracy vehicle lamp detection , light attenuation characteristic analysis , object-based methods , road traffic , traffic engineering computing , feature extraction , vehicle lamps , attenuation regions , vehicles , relative vertical lamp positions , real-time execution speed , real-time night-time vehicle detection algorithm
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