Proposed a Virtual Assistant as an Engineering Capstone Project, which aims to help visually impaired individuals in navigation.
Live feed captured from the Raspberry Pi is sent to Amazon Rekognition to extract information and insights.
Log Analysis is done on the feedback data with the help of AWS Athena to create stronger correlations in patterns and refine its navigation over time.
The driving force behind this project is to work on a Virtual Assistant by leveraging the power of cloud computing. The first and primary objective is to start with text detection (It can be a billboard, a book’s cover letter, etc.). After successful detection, the feedback on the detected text can be given by voice commands. The second phase will make this assistant more advanced by installing Alexa on the pi. We want to create custom Alexa skills to make our assistant more interactive. We could ask Alexa what the user saw at a particular time and date... This can be achieved using S3 and Athena. As all the data will be stored in S3 buckets, we could do a log analysis using simple SQL queries with the help of AWS Athena.
The AWS services we would like to leverage but are not limited to
- AWS Rekognition: To perform a visual analysis of the live video feed
- AWS Kinesis Video Streams: To transfer live video feed to AWS via Rasberry Pi
- AWS Lambda: To do serverless computing and interconnect all the services
- AWS S3: To store all the data
- AWS Alexa: To make the assistant more interactive
- AWS Polly: To perform the text-to-audio conversion
VisionScript.py has integrated all the services that are required for this project.
AWS documentation was always to the rescue.
Lambda - https://aws.amazon.com/lambda/
Boto3 - https://aws.amazon.com/sdk-for-python/
S3 - https://aws.amazon.com/s3/
Rekognition - https://aws.amazon.com/rekognition/
MIT License
Copyright (c) [2017] [James Q Quick]
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Linkedin - Jeevan Henry Dsouza
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Website - GitHub
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Linkedin - Deep Shah
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Website - GitHub