Our computer vision system tracks cricket balls from release to impact in real time. Here's how we achieved sub-millisecond detection latency on standard hardware.
Cricket presents unique challenges for ball tracking systems. Deliveries range from 60 km/h spin bowling to 150+ km/h pace, the ball changes appearance as it wears, and conditions vary enormously between a sunlit afternoon in Mumbai and a floodlit evening in Melbourne. Our system handles all of these variations while maintaining real-time performance on commercially available hardware.
The Detection Challenge
At 150 km/h, a cricket ball travels approximately 42 metres per second, roughly 70 centimetres per frame at 60 fps. The ball appears as a small, motion-blurred streak that can be confused with other fast-moving elements in the scene. Our detector uses a two-stage architecture: a fast region proposal network identifies candidate areas in under 0.3 milliseconds, followed by a precision classifier that confirms the ball's position with sub-pixel accuracy.
Pitch Map and Trajectory Analysis
Once we have reliable frame-by-frame positions, physics-based models reconstruct the full trajectory. By fitting the detected positions to equations that account for gravity, air resistance, seam position, and spin, we can predict pitch point, bounce trajectory, and the ball's path to the stumps. This enables automated analysis of line and length, swing movement, and seam deviation that matches what experienced commentators observe.
Running on Standard Hardware
Making this technology accessible required aggressive optimisation. Through model quantisation, custom CUDA kernels, and a carefully designed inference pipeline, the complete detection-tracking-analysis chain runs on a single modern GPU. This is a significant reduction from the multi-server setups that traditional broadcast tracking systems require, making the technology viable for domestic leagues, training facilities, and even local clubs.
The democratisation of sports analytics through affordable computer vision is still in its early stages. As hardware costs continue to fall and models become more efficient, we expect these tools to become standard equipment at every level of the game.
