VideoKifu, or the automatic transcription of a Go game
Mario Corsolini, Andrea Carta

TL;DR
VideoKifu presents an automated method for transcribing Go games from video streams, improving accuracy with multicore processing and asynchronous routines to handle errors and complex move scenarios.
Contribution
The paper introduces enhanced algorithms and asynchronous routines for automatic Go game transcription from video, leveraging multicore CPU capabilities for improved accuracy.
Findings
Improved grid and stone detection algorithms.
Effective handling of large goban movements and captures.
Enhanced accuracy in move sequence reconstruction.
Abstract
In two previous papers [arXiv:1508.03269, arXiv:1701.05419] we described the techniques we employed for reconstructing the whole move sequence of a Go game. That task was at first accomplished by means of a series of photographs, manually shot, as explained during the scientific conference held within the LIX European Go Congress (Liberec, CZ). The photographs were subsequently replaced by a possibly unattended video live stream (provided by webcams, videocameras, smartphones and so on) or, were the live stream not available, by means of a pre-recorded video of the game itself, on condition that the goban and the stones were clearly visible more often than not. As we hinted in the latter paper, in the last two years we have improved both the algorithms employed for reconstructing the grid and detecting the stones, making extensive usage of the multicore capabilities offered by modern…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Video Analysis and Summarization · Time Series Analysis and Forecasting
