增强现实国际象棋分析仪（ARChessAnalyzer）：使用卷积神经网络通过棋盘分割和棋子识别进行物理国际象棋游戏位置的设备内推断
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Network
摘要：
棋位分析是提高棋艺水平的重要手段。它需要进入一个国际象棋引擎，这是繁琐和容易出错的举动。我们提出了ARChessAnalyzer，一个完整的管道，从实时图像捕捉的物理象棋比赛，董事会和件识别，移动分析，最后到增强现实（AR）覆盖的国际象棋图的位置和移动的物理板。ARChessAnalyzer就像一个场景分析器-它使用传统图像和视觉技术的集成来分割场景（即象棋比赛），并使用卷积神经网络（CNN）来预测分割的棋子并将其组合在一起来分析比赛。这篇论文在第一个端到端集成棋盘的鲁棒检测和分割、棋子检测方面取得了先进的技术水平，该技术使用了手持式设备应用程序中的微调AlexNet CNN和国际象棋引擎分析器。整个国际象棋位置预测流水线的准确率为93.45%，从实时捕获到AR叠加需要3- 4.5秒。我们还验证了我们的假设，即ARChessAnalyzer在分析有效结果的所有董事会职位时比手动输入更快。我们希望这个应用程序提供的即时反馈将帮助世界各地的各级国际象棋学习者提高他们的比赛。
英文摘要：
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 handheld device app. The accuracy of the entire chess position prediction pipeline is 93.45\% and takes 3-4.5sec from live capture to AR overlay. We also validated our hypothesis that ARChessAnalyzer, is faster at analysis than manual entry for all board positions for valid outcomes. Our hope is that the instantaneous feedback this app provides will help chess learners worldwide at all levels improve their game.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
