2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) 2017
DOI: 10.1109/icecds.2017.8389893
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OpenCV and Matlab based car parking system module for smart city using circle hough transform

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Cited by 7 publications
(4 citation statements)
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“…So, in a nutshell, camera-viewing angles, camera resolutions, and the heights at which cameras are situated are critical parameters for efficient real-time parking detection. The prototype model gives the best results in the articles by Trivedi et al (2017Trivedi et al ( , 2020aTrivedi et al ( , 2020b, when the camera is situated at 90 degree angles because it focuses only on specific parking locations with parked vehicles. Here, we assume the camera is situated at a height of 3 meters from the ground surface and at an angle of around 40 to 50 degrees.…”
Section: Discussionmentioning
confidence: 99%
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“…So, in a nutshell, camera-viewing angles, camera resolutions, and the heights at which cameras are situated are critical parameters for efficient real-time parking detection. The prototype model gives the best results in the articles by Trivedi et al (2017Trivedi et al ( , 2020aTrivedi et al ( , 2020b, when the camera is situated at 90 degree angles because it focuses only on specific parking locations with parked vehicles. Here, we assume the camera is situated at a height of 3 meters from the ground surface and at an angle of around 40 to 50 degrees.…”
Section: Discussionmentioning
confidence: 99%
“…A mono-camera-based parking module is explained by Davarci et al (2018) with the image-processing method, but a real-time parking demonstration is not presented in that study. A prototype model for a smart city parking system is established in Trivedi et al (2017Trivedi et al ( , 2020aTrivedi et al ( , 2020b; however, work for a real-time parking management system is not demonstrated there. A canny edge-detection-based real-time parking management system is explained in Trivedi et al (2020aTrivedi et al ( , 2020b with parking characteristics.…”
Section: Literature Surveymentioning
confidence: 99%
“…Before detection, the image needs to be preprocessed, such as filtering, image segmentation, morphological processing, edge detection, etc., as shown in Figure 15. For circle detection in images, Hough circle transformation is a circle detection algorithm widely used by relevant scholars [17,18]. However, the standard Hough transform algorithm is complicated and inefficient.…”
Section: Image Recognitionmentioning
confidence: 99%
“…The detection process is to obtain the gradient direction of the image after edge detection, and then determine a line segment along the gradient direction according to the set distance range. The pixels with the largest For circle detection in images, Hough circle transformation is a circle detection algorithm widely used by relevant scholars [17,18]. However, the standard Hough transform algorithm is complicated and inefficient.…”
Section: Image Recognitionmentioning
confidence: 99%