Have you ever wanted to perform person detection on Arduino or ESP32 boards? Found it difficult to custimize the sketch to suit your needs? Fear no more!
Person detection on Arduino and ESP32 microcontrollers doesn't have to be difficult: with the right library, you only need 3 lines of code to perform state-of-the-art person detection. You use TensorFlow Neural Networks without any boilerplate and verbose code using the EloquentTinyML library.
This will be a short post where I introduce a new addition to the Arduino Eloquent library aimed to make video streaming from an ESP32 camera over HTTP super easy. It will be the first component of a larger project I'm going to implement.
If you liked my post about ESP32 cam motion detection, you'll love this updated version: it's easier to use and blazing fast!
We continue exploring the endless possibilities on the MicroML (Machine Learning for Microcontrollers) framework on Arduino and ESP32 boards: in this post we're back to image classification. In particular, we'll distinguish handwritten digits using an ESP32 camera.
Do you have an ESP32 camera?
Want to do image recognition directly on your ESP32, without a PC?
In this post we'll look into a very basic image recognition task: distinguish apples from oranges with machine learning.
Do you have an ESP32 camera? Do you want to do motion detection WITHOUT ANY external hardware?
Here's a tutorial made just for you: 30 lines of code and you will know when something changes in your video stream 🎥
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