I thought to try out person detection with Edge Impule and Neuton. Unfortunately, I didn't manage to check Neuton completely for free, thus I gave up on that idea.

Regarding Edge Impulse - it is very intuitive to use and support is very quick and helpful. There are many tutorials available, courses by Shawn Hymel on his Youtube channel or on Coursera. There is also a book TinyML coockbook by Gian Marco Iodice - thus I will focus on results.

I made a project for person_detection and used the same dataset as I did so far. What I find cool about Edge Impulse is that you can have a look at the architecture proposed and additionally you are free to make the changes.
Training results:

While looking at the model architecture, it is very similar to what I have been describing in previous posts. The differences lie in the hyperparameters. Interesting enough, when I copied the exact code from Edge Impulse, I didn't receive the same results.

After the training with Edge Impulse - let's generate the code for Arduino! Obviously the code needs to be very generic, thus it is a little bit harder to follow. For easier comparison, I added the parts to check the time needed for inference and the transfer if image and the results to the PC.
Impressive size!!!

Let's see the execution time:

Time taken by readout and data curation: 2742 ms
Time taken by inference: 417 ms

I noticed that in the generated code, the image retrieved is RGB (585) with resolution 160X120 and 1 fs (while I trained the model with grayscale images). Additionally, the image is only cropped to retrieve 96x96 image size. While in my code, I used grayscale, QCIF, 5fs - the image was first scaled and then cropped. I tried to modify the Edge Impulse example to increase the frame rate, but after that I stopped retrieving any images.

And in the video - both models in action!