This research project investigates the application of several computer vision
techniques for playing card detection and recognition in the context of the
popular casino game, blackjack. The primary objective is to develop a robust
system that is capable of detecting and accurately classifying playing cards in
real-time, and displaying the optimal move recommendation based on the given
image of the current game. The proposed methodology involves using K-Means for
image segmentation, card reprojection and feature extraction, training of the
KNN classifier using a labeled dataset, and integration of the detection system
into a Blackjack Basic Strategy recommendation algorithm. Further, the study
aims to observe the effectiveness of this approach in detecting various card
designs under different lighting conditions and occlusions. Overall, the
project examines the potential benefits of incorporating computer vision
techniques, with a specific focus on card detection, into commonly played games
aiming to enhance player decision-making and optimize strategic outcomes. The
results obtained from our experimental evaluations with models developed under
considerable time constraints, highlight the potential for practical
implementation in real-world casino environments and across other similarly
structured games.

这项研究项目旨在应用计算机视觉技术来进行二十一点等赌场游戏中的纸牌检测和识别，以开发一个能够实时检测和准确分类纸牌，并基于当前游戏图像显示最佳移动建议的稳健系统。该研究探讨了使用 K-Means 进行图像分割、纸牌投影重建和特征提取、使用带标签数据集训练 KNN 分类器，并将检测系统整合到二十一点基本策略建议算法中的方法。此外，该研究旨在观察在不同光照条件和遮挡下，该方法在检测不同纸牌设计方面的有效性。总体而言，该项目探讨了将计算机视觉技术，特别是纸牌检测，应用于常见游戏中以增强玩家决策和优化策略结果的潜在优势。在考虑到时间限制的情况下，通过实验评估获得的结果突显了在真实赌场环境和其他类似结构的游戏中进行实际应用的潜力。

优化二十一点策略推荐器：关于增强游戏体验的计算机视觉集成的综合研究

Optimal Blackjack Strategy Recommender: A Comprehensive Study on  Computer Vision Integration for Enhanced Gameplay

Online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout
the agent's lifetime. However, the growing affordability of data storage
highlights a broad range of applications that do not adhere to these
assumptions. In these cases, the primary concern lies in managing computational
expenditures rather than storage. In this paper, we target such settings,
investigating the online continual learning problem by relaxing storage
constraints and emphasizing fixed, limited economical budget. We provide a
simple algorithm that can compactly store and utilize the entirety of the
incoming data stream under tiny computational budgets using a kNN classifier
and universal pre-trained feature extractors. Our algorithm provides a
consistency property attractive to continual learning: It will never forget
past seen data. We set a new state of the art on two large-scale OCL datasets:
Continual LOCalization (CLOC), which has 39M images over 712 classes, and
Continual Google Landmarks V2 (CGLM), which has 580K images over 10,788 classes
-- beating methods under far higher computational budgets than ours in terms of
both reducing catastrophic forgetting of past data and quickly adapting to
rapidly changing data streams. We provide code to reproduce our results at
https://github.com/drimpossible/ACM.

本文提出了一种用于在线连续学习问题的简单算法，该算法采用 kNN 分类器和通用预先训练的特征提取器，可以在小的计算预算下紧凑存储和利用整个数据流，既能减少过往数据的灾难性遗忘，同时也能快速适应快速变化的数据流，同时在 Continual LOCalization (CLOC) 和 Continual Google Landmarks V2 (CGLM) 两个大规模 OCL 数据集上取得了最新的技术水平。