The solution is built with TensorFlow, a handy and flexible computing system
Image recognition is very widely used in machine learning. There are many different approaches and solutions to it, but none of them fitted our needs. We needed a completely local solution running on a tiny computer to deliver the recognition results to a cloud service. This article describes our approach to building an object recognition solution with TensorFlow.
The script will skip frames from the camera during evaluation and take the next available frame when the previous evaluation step has completed. For recorded video it won’t skip any frames. For most purposes it’s ok to skip some frames to keep the process running in real-time.
And of course, it would be nice to integrate an IoT service into this project as well as to deliver the recognition results somewhere in a place where other services can have access to them. There is one more demo script, python daemon.py, that will run a simple server that will display a video stream from a webcam with predictions on .
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Gurupriyan is a Software Engineer and a technology enthusiast, he’s been working on the field for the last 6 years. Currently focusing on mobile app development and IoT.