Real-Time Snooker Table Understanding with PyTorch, MobileNet-SSD, and Jetson Devices

This talk walks through how I built a full computer-vision pipeline that detects snooker balls, cues, cushions, pockets, and ball trajectories using PyTorch and MobileNet-SSD, deployed on Jetson Nano devices. The goal of the system was to convert raw video into structured table state: precise x/y coordinates of every ball, detection of ball disappearance, and automatic scoring.

The project was developed in 2022 during COVID and required solving several real-world challenges: wide-angle camera distortion, severe dataset imbalance across ball colors, inconsistent bounding-box scales, and motion blur from fast-moving cues and balls. Models were trained on H100 GPUs and optimized to run efficiently on edge hardware (Jetson Nano) for low-latency inference.

I will cover the dataset strategy, model architecture choices, augmentation methods, SSD tuning specifics, and the geometric logic that converts detections into scoring and gameplay interpretation.