Sub-millisecond object detection, trajectory prediction, and spin analysis. A deep dive into the computer vision pipeline powering our sports analytics platform.
Tracking a ball in real time during live sports is one of the most demanding computer vision challenges. A cricket ball at 150 km/h covers about 4 centimetres per millisecond. At standard broadcast frame rates, the ball appears as a blur spanning just a handful of pixels. Our sports vision system achieves 99.5% detection accuracy at these speeds, and here is how.
The Detection Pipeline
Our pipeline operates in three stages. First, a lightweight detection model scans each frame to identify candidate regions where the ball might be. This model is optimised for speed, running inference in under 0.5 milliseconds per frame on modern GPUs. Second, a refinement model analyses each candidate region at higher resolution to confirm detection and extract precise position coordinates. Third, a tracking module links detections across frames to build trajectories.
The key innovation is our multi-scale architecture. The ball looks very different at various points in its trajectory: large and sharp near the camera, small and blurred at distance. Our model processes each frame at multiple resolutions simultaneously, ensuring consistent detection regardless of the ball's apparent size.
Trajectory Prediction and Spin
Raw position data becomes much more useful when combined with physics-based trajectory modelling. By fitting detected positions to ballistic equations that account for gravity, air resistance, and spin, we can predict the ball's future path with high accuracy. Spin estimation uses the visual rotation of the ball's seam across frames, enabling analysis of swing, spin rate, and deviation.
Making It Real-Time
Achieving real-time performance requires careful optimisation at every level. We use quantised models deployed on CUDA-optimised inference engines, custom memory management to avoid garbage collection pauses, and a pipelined architecture where detection, tracking, and visualisation run concurrently. The end result is a system that processes 60 frames per second with total pipeline latency under 16 milliseconds.
