Vehicle detection has become an important and challenging aspect of a safe transportation system in the nighttime as most accidents occur at night due to the absence of night lighting conditions. Many algorithms detect the vehicles at nighttime based on the headlights of the vehicles, but it does not apply in the daytime or when the headlights were off. These algorithms also find difficulty when vehicles are in no motion or when it is in parking in the night. In this paper, two approaches, image transformation (IMT) approach and the vehicle detection (VD) approach, are used to detect the vehicles in the nighttime. IMT approach is built based on OpenCV and Gamma correction. This approach is used to change the illumination of the images which are not clearly visible or very dark images. Gamma correction increases brightness of an image. Second, the OD module uses the Haar cascade classifier. The patterns in this classifier can identify the vehicle/object based on those patterns. In this paper, our approach will identify the vehicles in night which are parked or headlights were off, based on patterns like in the daytime by increasing the brightness of the images, to avoid the confusion of headlights.
Vehicle Spotting in Nighttime Using Gamma Correction
Lect. Notes in Networks, Syst.
2022-08-02
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Vehicle Classification in Nighttime Using Headlights Trajectories Matching
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