Many high-end sophisticated devices are being replaced by small single-board IoT devices. It is estimated that by the year 2025 there will be about 75 billion IoT connected devices worldwide. This project buttresses the earlier statements. The project will be focused on how to deploy a complex AI face-recognition model on a simple, less power consumption, single-board IoT device Raspberry Pi3. Also, to simplify the whole process of deploying the AI model on Raspberry Pi3 by dockerizing. By just pulling the respective docker image and running it on the end device (Raspberry Pi3), we can make face recognition happen. Finally, the obtained results will be sent to the cloud (Fiware) using NGSI API. Instead of sending the whole video feed to the Cloud platform and performing the computing there, we managed to perform the computing at the edge and then send the results to the cloud and making the results accessible to requested users.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-18418 |
| Date | January 2019 |
| Creators | Mamidi, Sai Prakash, Ummadisetty, Yogitha Manasa |
| Publisher | Blekinge Tekniska Högskola, Blekinge Tekniska Högskola |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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