Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Network
Anav Mehta

TL;DR
ARChessAnalyzer is an innovative app that captures live images of a physical chess game, recognizes the board and pieces using CNNs, and overlays move analysis in AR, significantly speeding up game analysis and aiding learning.
Contribution
It presents the first end-to-end system integrating scene segmentation, CNN-based piece recognition, and move analysis on a handheld device for real-time chess position evaluation.
Findings
Achieved 93.45% accuracy in position prediction
Analysis time reduced to 3-4.5 seconds from capture to overlay
Faster than manual entry for all tested positions
Abstract
Chess game position analysis is important in improving ones game. It requires entry of moves into a chess engine which is, cumbersome and error prone. We present ARChessAnalyzer, a complete pipeline from live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to Augmented Reality (AR) overlay of the chess diagram position and move on the physical board. ARChessAnalyzer is like a scene analyzer - it uses an ensemble of traditional image and vision techniques to segment the scene (ie the chess game) and uses Convolution Neural Networks (CNNs) to predict the segmented pieces and combine it together to analyze the game. This paper advances the state of the art in the first of its kind end to end integration of robust detection and segmentation of the board, chess piece detection using the fine-tuned AlexNet CNN and chess engine analyzer in a…
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Taxonomy
TopicsImage and Object Detection Techniques · Robotics and Sensor-Based Localization · Advanced Vision and Imaging
