Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/7420
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dc.contributor.authorBanerjee, S.-
dc.contributor.authorAshwin, T.S.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.date.accessioned2020-03-30T09:59:03Z-
dc.date.available2020-03-30T09:59:03Z-
dc.date.issued2019-
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , pp.931-935en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7420-
dc.description.abstractIn today's world we need to maintain safety and security of the people around us. So we need to have a well connected surveillance system for keeping active information of various locations according to our needs. A real-time object detection is very important for many applications such as traffic monitoring, classroom monitoring, security rescue, and parking system. From past decade, Convolutional Neural Networks is evolved as a powerful models for recognizing images and videos and it is widely used in the computer vision related work for the best and most used approach for different problem scenario related to object detection and localization. In this work, we have proposed a deep convolutional network architecture to automate the parking system in smart campus with modified Single-shot Multibox Detector (SSD) approach. Further, we created our dataset to train and test the proposed computer vision technique. The experimental results demonstrated an accuracy of 71.2% for the created dataset. � 2019 IEEE.en_US
dc.titleAutomated Parking System in Smart Campus Using Computer Vision Techniqueen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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