Bio-inspired camera for surveillance in IoT
Using 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. Thi...
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2020
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sg-ntu-dr.10356-1403062023-07-07T18:48:45Z Bio-inspired camera for surveillance in IoT Gunasekeran, Ruvendren Arindam Basu School of Electrical and Electronic Engineering arindam.basu@ntu.edu.sg Engineering::Bioengineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Using 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-28T02:03:09Z 2020-05-28T02:03:09Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140306 en A2019-191 application/pdf Nanyang Technological University |
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Engineering::Bioengineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gunasekeran, Ruvendren Bio-inspired camera for surveillance in IoT |
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Using 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. |
author2 |
Arindam Basu |
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Arindam Basu Gunasekeran, Ruvendren |
format |
Final Year Project |
author |
Gunasekeran, Ruvendren |
author_sort |
Gunasekeran, Ruvendren |
title |
Bio-inspired camera for surveillance in IoT |
title_short |
Bio-inspired camera for surveillance in IoT |
title_full |
Bio-inspired camera for surveillance in IoT |
title_fullStr |
Bio-inspired camera for surveillance in IoT |
title_full_unstemmed |
Bio-inspired camera for surveillance in IoT |
title_sort |
bio-inspired camera for surveillance in iot |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/140306 |
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1772827709414572032 |