Abstract
Vehicle detection technology is the key technology of intelligent transportation systems, attracting the attention of many researchers. Although much literature has been published concerning daytime vehicle detection, little has been published concerning nighttime vehicle detection. In this study, a nighttime vehicle detection algorithm, consisting of headlight segmentation, headlight pairing and headlight tracking, is proposed. First, the pixels of the headlights are segmented in nighttime traffic images, through the use of the thresholding method. Then the pixels of the headlights are grouped and labeled, to analyze the characteristics of related components, such as area, location and size. Headlights are paired based on their location and size and then tracked via a tracking procedure designed to detect vehicles. Vehicles with only one headlight or those with three or four headlights are also detected. Experimental results show that the proposed algorithm is robust and effective in detecting vehicles in nighttime traffic.
Highlights
Vehicle detection is an important problem in many related applications, such as self-guided vehicles, driver assistance systems, intelligent parking systems, or in the measurement of traffic parameters, such as vehicle count, speed and flow
Tsai et al (2007) proposed an approach for detecting vehicles using still images, based on color and edge features. This approach can detect vehicles without motion information, allowing static or slowly moving vehicles to be efficiently detected from image sequences
Experimental results show that the proposed algorithm can robustly and effectively detect vehicles in complicated nighttime traffic conditions
Summary
Vehicle detection is an important problem in many related applications, such as self-guided vehicles, driver assistance systems, intelligent parking systems, or in the measurement of traffic parameters, such as vehicle count, speed and flow. Tsai et al (2007) proposed an approach for detecting vehicles using still images, based on color and edge features. Most of the features employed for vehicle detection, such as color, shadows, edges and motion information, are difficult or impossible to extract in dark or nighttime situations. To detect vehicles in nighttime traffic conditions, Zhang et al (2012) applied a reflection intensity map and a suppressed reflection map, based on the analysis of the light attenuation model, in order to extract the headlights. Pixels of headlights are extracted from the captured image sequences by utilizing the thresholding method. Experimental results show that the proposed algorithm can robustly and effectively detect vehicles in complicated nighttime traffic conditions
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