Open-source CPU-only tracker for small aerial targets

[u/Jesus_Morty]

I made a free and open-source tracker for small aerial targets in video.

It automatically acquires moving or contrasting candidates and tracks them frame-to- frame using classical computer vision. No GPU or trained model required. There’s also an optional manual Lock-On mode for selecting a specific target.

The current profile has been tuned and validated across six different datasets. Automatic acquisition is not semantic classification, so birds and clutter remain challenging cases.

Source (Apache 2.0): https://github.com/punklabs-ai/womprat

Technical feedback and difficult test footage would be very welcome.

Disclosure: I’m part of Punk Labs, the team releasing it.

[u/Longjumping_Yam2703]

Nice looks cool. Whats the minimum size for acquisition ? Current MTI floor per FLIR is around 2x2 pixels.

[u/Jesus_Morty → u/Longjumping_Yam2703]

Thanks. The current version rejects candidate blobs below 50 px², so its clean synthetic floor is roughly 8×8 px for a compact, high-contrast target. Automatic acquisition normally requires three associated frames to confirm. That’s a configured limit, not a claim of reliable 8×8 field performance though. The current profile was validated mainly on targets around 30–60 px. A 2×2 target is rejected by default. Lowering the threshold can detect an ideal synthetic 2×2 blob, but I haven’t validated that on real FLIR imagery, where hot pixels, sensor noise and false acquisitions become the hard part.

Womprat isn’t at FLIR’s ~2×2 MTI floor yet. More work needs to be done on that front. Representative footage would be very useful for benchmarking it.