This is seriously impressive. The jump from 61% to 99.6% ball coverage without retraining is a great example of why system design often matters more than chasing a bigger model.
I’m building a similar football analytics system, so the combination of detector ensembling, temporal continuity, stereo triangulation, and offline bidirectional smoothing is especially interesting. The shared data layer feeding Three.js, Rerun, and Blender is also a very clean architecture.
Would love to see a deeper technical breakdown of the identity tracking and 3D reconstruction pipeline.
Been working on a football video to 2D map pipeline for a while now ⚽️
Still a lot to improve specially regarding the detection model, but this is the latest result
Big thanks to @roboflow@skalskip92 and the SAM 3 team for helping make this possible.
#computervision#yolo
@IgorOcto@roboflow@skalskip92 Thanks a lot. Yes, I am becoming more involved, and I am planning to expand the system so it captures team possession, each player’s speed, shots, and similar details.
@thdxr Been working on a football video to 2D map pipeline for a while now ⚽️
Still a lot to improve specially regarding the detection model, but this is the latest result
@sama Built this with 5.6 sol
I’m working on a pipeline that turns football match videos into 2D tactical maps for analysis.
Still a work in progress, but every test is getting closer.
Starting with Iraqi football because this kind of tool is badly needed here.
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