Product
AI ball tracking for cricket broadcast - frame-accurate, DRS-grade, GPU-powered.
The problem
Broadcast-quality ball tracking has been expensive, proprietary, and out of reach for most cricket.
What it does
Tracks the ball frame by frame from standard broadcast feeds, reconstructs trajectory, and delivers DRS-style output in real time.
Watch the demo
Illustrative placeholder
Illustrative only, not live broadcast footage. The real Ball Track demo replaces this frame at launch.
Why it matters
Our credibility beachhead - if we can track a small, fast object this precisely, the rest of our vision work is trivial by comparison.
Proof
Backed by NVIDIA Inception, and built on GPU-accelerated inference pipelines engineered for frame-by-frame precision under real broadcast conditions.
FAQ
AI ball tracking uses computer vision to follow the ball frame by frame across a delivery, reconstructing its full flight path from standard broadcast camera angles - the technology behind ball-by-ball trajectory graphics and DRS-style review.
DRS-grade tracking needs frame-level precision on a small, fast-moving object, often from partial or obstructed camera views. Ball Track is built specifically for that problem, which is a much harder computer vision task than tracking a person or a vehicle.
Yes. Ball Track is designed to run on standard broadcast feeds rather than requiring a dedicated multi-camera rig, which is what has historically made broadcast-grade ball tracking expensive and hard to access.
Ball Track reconstructs ball trajectory and delivers DRS-style output in real time - the same category of system used to support on-field umpire decision review in broadcast cricket.
Tracking a small, fast object this precisely is one of the harder problems in computer vision. It's our proof point: if we can do this reliably, the rest of our vision work - safety monitoring, traffic enforcement, CCTV analytics - is comparatively simpler.
AI ball tracking for cricket broadcast - frame-accurate, DRS-grade, GPU-powered.
