Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140306
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dc.contributor.authorGunasekeran, Ruvendrenen_US
dc.date.accessioned2020-05-28T02:03:09Z-
dc.date.available2020-05-28T02:03:09Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/140306-
dc.description.abstractUsing an event-based camera sensor instead of a frame-based camera sensor has many benefits such as reduced power consumption and reduced file output memory size. Adding cognitive abilities onto an event-based camera sensor would further reduce the information that this sensor needs to transmit. This paper seeks to combine the advances in deep neural networks for frame-based videos with the efficiency of event-based sensors. This will be done by comparing existing algorithms used in frame-based videos to identify objects in event-based camera output. Additionally, this paper seeks to reduce the complexity of these algorithms to ensure the practical implementation of the algorithm in resource constrained settings.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA2019-191en_US
dc.subjectEngineering::Bioengineeringen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleBio-inspired camera for surveillance in IoTen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorArindam Basuen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailarindam.basu@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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