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2017 | OriginalPaper | Buchkapitel

IoT-Based Smart Monitoring System Using Automatic Shape Identification

verfasst von : Stanisław Deniziak, Tomasz Michno, Paweł Pieta

Erschienen in: Proceedings of the 2015 Federated Conference on Software Development and Object Technologies

Verlag: Springer International Publishing

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Abstract

In conventional monitoring systems video previews from stationary cameras are overseen only by a human supervisor, who may easily overlook alarming events recorded by a camera. Because surveillance system must be reliable, its capabilities can be improved by applying computer vision algorithms to a video signal in order to detect objects in an automated fashion. Also its autonomy can be extended by the use of mobile robots capable of monitoring tight and occluded areas and by the use of smart cameras with integrated embedded systems. In this paper we introduce an architecture of the autonomous monitoring system based on object shape detection. Our approach is conformable with the concept of Internet of things. It consists of the set of smart objects with video sensors, controlled by the Shape Identification Cloud. Our work is aimed at building the real-time system efficient at reliable recognition of objects on the basis of their approximate shape and with the option to be used as a web service in a cloud. To monitor the environment the system uses mobots equipped with video sensors as well as surveillance cameras capable of remote position control. For object identification task we use the Query by Shape (QS) method which decomposes objects into simple graphical primitives like lines, circles, ellipses etc. and then it identifies them in a shape database.

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Titel: IoT-Based Smart Monitoring System Using Automatic Shape Identification
verfasst von: Stanisław Deniziak
Tomasz Michno
Paweł Pieta
Verlag: Springer International Publishing
Buch: Proceedings of the 2015 Federated Conference on Software Development and Object Technologies
Print ISBN: 978-3-319-46534-0

Electronic ISBN: 978-3-319-46535-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-46535-7_1

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