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Smart city article details

Title	Using Deep Neural Networks To Quantify Parking Dwell Time
ID_Doc	60526
Authors	Ribas M.M.; Mendes H.B.; De Oliveira L.E.; Zanlorensi L.A.; De Almeida P.L.
Year	2024
Published	Proceedings - 2024 International Conference on Machine Learning and Applications, ICMLA 2024
DOI	http://dx.doi.org/10.1109/ICMLA61862.2024.00232
Abstract	In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline. © 2024 IEEE.
Author Keywords	Deep Learning; Siamese Network; Smart Cities

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