Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4150
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dc.contributor.authorPatra, Jayaprakash-
dc.contributor.authorPanda, Satyajit-
dc.contributor.authorNegi, Vipul Singh-
dc.contributor.authorChinara, Suchismita-
dc.date.accessioned2023-12-20T04:53:43Z-
dc.date.available2023-12-20T04:53:43Z-
dc.date.issued2023-11-
dc.identifier.citation6th IFIP International Internet of Things (IOT) Conference, Dallas-Fort Worth Metroplex, Texas, USA, 2-3 November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4150-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractA Smart Parking system has a lot of components such as an automated parking infrastructure, sensors, and a navigation system. For the implementation of the navigation system in smart parking, a 3D floor map is required. A 3D view of maps is always better than traditional maps, but making a 3D model comes at a cost and requires specialized tools. Infrastructures such as hospitals, offices usually have the little luxury when it comes to maintaining their parking spaces, and the proposed system is providing a simple yet effective solution for this problem in this paper. Till now, images are two-dimensional, and tools like Lidar or Kinect are used to get the depth element right. However, to make the floor construction handy, portable, and lightweight a smartphone image-based approach is proposed here to make a 3D model of indoor parking lots. The pillars and the separation walls between parking spaces are easy to identify using deep learning models. A convolution neural network-based architecture was used for object detection. The main problem that remains is to calculate the depth of the objects in the image. Here in this paper, a successful approach is proposed to overcome the problem of finding depth in images.en_US
dc.subjectSmart Parking Systemsen_US
dc.subject3D Mappingen_US
dc.subjectComputer Visionen_US
dc.titleComputer Vision based 3D model floor construction for Smart Parking Systemen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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