Atm theft investigation using convolutional neural network

Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/15157

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DC Field	Value	Language
dc.contributor.author	Satish Y.C.
dc.contributor.author	Rudra B.
dc.date.accessioned	2021-05-05T10:16:35Z	-
dc.date.available	2021-05-05T10:16:35Z	-
dc.date.issued	2021
dc.identifier.citation	Advances in Intelligent Systems and Computing , Vol. 1177 , , p. 21 - 29	en_US
dc.identifier.uri	https://doi.org/10.1007/978-981-15-5679-1_3
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/15157	-
dc.description.abstract	Image processing in a surveillance video has been a challenging task in research and development for several years. Crimes in Automated Teller Machine (ATM) is common nowadays, in spite of having a surveillance camera inside an ATM as it is not fully integrated to detect crime/theft. On the other hand, we have many image processing algorithms that can help us to detect the covered faces, a person wearing a helmet and some other abnormal features. This paper proposes an alert system, by extracting various features like face-covering, helmet-wearing inside an ATM system to detect theft/crime that may happen. We cannot judge theft/crime as it may happen at any time but we can alert the authorized persons to monitor the video surveillance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.	en_US
dc.title	Atm theft investigation using convolutional neural network	en_US
dc.type	Conference Paper	en_US
Appears in Collections:	2. Conference Papers

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