Abstract
By considering the ever-increasing traffic in metropolitan areas, vehicle parking has become a great hindrance, especially while finding the available parking space nearby any office space or shopping mall, which is located on the narrow roadways. As the attempt to manually search for a parking slot consumes more time, commercial parking slots are designed to balance the demand and availability of vehicle parking spaces. Since constructing and monitoring a private parking space requires more money and workforce, parking charge has become very expensive. Due to the non-affordability of drivers, they waste more time in looking for empty parking slots. To overcome these challenges, the proposed research work helps to automatically identify the empty parking spaces, so that the car can be parked even in the most comfortable spot via video image processing and neural networks techniques, which develops a parking management software that actually identifies the existence of parking areas. The data from video footage is used to train the Mask R-CNN architecture, where a computer vision image recognition model is used to automatically identify the parking spaces. To label the car parking place mostly on the source images of a whole parking lot, a pre-processed region-based convolutional neural network (Mask R-CNN) is used. All of this could be solved by impelmenting a smart application, which could also send a text information to the customer, whenever a parking slot becomes available. Only at end of the day, it is required to have an appropriate and possible approach for solving all parking issues in and around the neighbourhood.
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Manjula, G., Govinda Rajulu, G., Anand, R., Thirukrishna, J.T. (2022). Implementation of Smart Parking Application Using IoT and Machine Learning Algorithms. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_18
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