Build a Chess Game Recorder with Computer Vision

The article describes the development of Clio lite, a cost-effective and portable device designed to record and analyze chess games using computer vision techniques, circumventing the high costs of sensor-based systems. The project aims to make chess game recording more accessible by employing computer vision and machine learning to detect chessboard boundaries and recognize chess pieces, despite challenges such as variations in appearance and lighting conditions. Clio lite features a fixed camera angle that simplifies these problems, and its design includes a microcontroller and a wide-angle camera to minimize costs. The article details the methodology for chessboard detection, using tools like OpenCV and YOLOv5 for creating a homography matrix and estimating image transformations. The system's position recognition leverages a square occupancy-based method to localize chess pieces, with a companion mobile app allowing users to stream and replay games. The creators are keen to enhance the software's robustness and expand its capabilities to other games like Chinese Chess and Go.