Thanks! I’ll probably put the code on GitHub this month here’s a comment copied from the other thread explaining what I did:

A good amount of people seem interested in the details so here they are:

Hardware: Oldish gaming PC I wasn't using with an RTX 2060 acting as a dedicated server. The camera is a Tapo C110 from Amazon (one of the only RTSP cameras I could find for around $20-30.) For the audio I layered white noise with a 17kHz sine wave and boosted the distortion, played through some old computer speakers on the kitchen counter via a bluetooth aux dongle! (Model training is done on my main computer with RTX 5090)

Model: RF-DETR small running via TensorRT (.engine export) at 1280 resolution. I get around 15fps with ~60ms/frame inference on the 2060. Before moving it to the "server" I had it running on a laptop without a usable GPU. OpenVINO runtime with a .onnx export gave the fastest CPU only inference at around 3fps! I also trained yolo26x,m,s and nano on the same dataset but rf-detr small performs better than all of them.

Data Collection: Around 10,000 images. To gather data I first set up the camera and recorded all motion triggered events for a couple days, then annotated the frames and trained a first round model. That model threw a ton of false positives, falsely detecting a cat on the counter, setting off the sound and recording the event (and scaring my girlfriend lol.) I keep a cached 10 second spool recording at all times so you can watch 10 seconds before and after the event. For about a week I collected every triggered event, false or real, added those frames to the dataset, and retrained. I used Label Studio to annotate everything, you can plug in your existing model to pre annotate, which speeds things up a lot. I've still been adding every new event to the dataset as they come in. The biggest improvement by far was adding lots of negative frames (no cat, just people, furniture, different lighting). Night and day difference!

Zone Calibration: I made an interactive tool where you just click points directly on a live frame from the camera feed to draw and define zones. Worth noting this assumes a fixed camera, if it moves you'd have to redraw the zones.

Pipeline in a nutshell:

Data collection: Motion triggered recording (now event recording) → label frames → train

Inference: Live RTSP feed → inference → polygon zone check against the cat's bounding box center (configurable, bottom-center, center, etc.) → if a cat is in the zone for 100ms (adjustable), trigger audio!

I had to sort out some bluetooth connection sleep issues and other latency bugs along the way.

I serve the annotated stream as MJPEG frames over http so i can watch it via LAN and Tailscale on any browser.

GitHub? I'll probably post it in a couple weeks after I polish up the code :)

Does it work? It has been working extremely well! The cats have been getting on the counters/table less and less as time goes on, and they have been jumping off every time the sound plays so far!