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@article{159290,
        author = {Riddhi Wakde and Atharva Mestry and Onkar Kanse and Jaychand Upadhyay},
        title = {Automated Parking Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {824-830},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159290},
        abstract = {Automated parking analysis is an emerging research area in computer vision that aims to develop efficient parking solutions for modern urban environments. This paper proposes a methodology for automated parking analysis using OpenCV. The proposed methodology involves segmenting the parking lot into rectangular blocks to specify parking spots, acquiring live footage from a top-down view camera of the parking lot, and employing image processing techniques to count the number of foreground and background pixels to determine the occupancy status of each parking spot. To mitigate the influence of environmental factors, a threshold value of 900 pixels is used to differentiate between foreground and background pixels. If the count of foreground pixels in a block exceeds 900, the spot is considered occupied; otherwise, if the count of foreground pixels is less than 900, the spot is considered vacant. This system displays the number of available parking spots and total parking spots on a screen.},
        keywords = {Image Processing, OpenCV, GreyScale, Segmentation, Dilation.},
        month = {},
        }