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The automobile industry has expanded rapidly with the growing population over the past few decades. With this exponential increase, there is a seamless flow of the traffic causing congestion and sometimes mismanagement of this heavy traffic. To solve this problem some hardware solutions have been introduced previously. These hardware-based solutions use sensors installed at various locations and monitor the traffic and status of the parking lots available at different sites. However, these are less scalable, difficult to maintain, and also costly at times. In the same way, there are also data-driven solutions being brought into the picture which use the data from the surveillance cameras installed at the parking areas. This overcomes the drawbacks of the sensor-based system but is not up to the mark. In order to overcome all these limitations, this paper brings up a solution that uses a deep learning model to classify and analyze the parking lots.
Keywords:
Deep learning, OpenCV, Parking lot detection, video processing, real-time detection
Cite Article:
"Monitoring Empty and Occupied Parking Spots: A Deep Learning Based Solution", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b149-b155, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504119.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator