Open Access

AFFORDABLE VISION-BASED SYSTEMS FOR REAL-TIME CHESSBOARD DIGITIZATION

https://doi.org/10.55640/ijmcsit-v02i01-02

pdf

Dr. Ahmed R. Mostafa ¹ , Prof. Mahmoud A. Taha ¹ ,

⁴ Department of Computer Systems and Robotics, Alexandria University, Egypt

⁴ Department of Computer Engineering, Alexandria University, Egypt

Abstract

The automatic recognition of chessboard states has significant applications in various domains, from enhancing online chess platforms and educational tools to enabling robotic interaction. While high-performance vision systems and complex robotic setups can achieve this, their cost and complexity often limit widespread adoption. This paper explores the feasibility and methodology for developing affordable, vision-based systems for real-time chessboard digitization. We leverage advancements in deep learning, particularly lightweight Convolutional Neural Networks (CNNs), combined with accessible embedded platforms. The proposed approach integrates image acquisition, chessboard localization, and individual chess piece recognition, culminating in a standardized digital representation of the board state. Our findings demonstrate that acceptable levels of accuracy and real-time performance can be achieved on low-cost hardware, making automatic chess digitization more accessible for a broader range of applications.

Keywords

Chessboard digitization, real-time processing, vision-based systems, affordable hardware

References

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov 1998. [Online]. Available: https://doi.org/10.1109/5.726791

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105. [Online]. Available: https://doi.org/10.1145/3065386

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. [Online]. Available: https://doi.org/10.1109/CVPR.2016.90

V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proceedings of the IEEE, vol. , no. 12, p. 2295–2329, 2017. [Online]. Available: http://doi.org/10.1109/JPROC.2017.2761740

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. [Online]. Available: https://doi.org/10.1109/CVPR.00474

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807. [Online]. Available: https://doi.org/10.1109/CVPR.2017.195

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. [Online]. Available: <https://doi.org/10.1109/cvpr.2018.>

Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: Analysis, applications, and prospects,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999–7019, 2022. [Online]. Available: https://doi.org/10.1109/TNNLS.2021.3084827

M. A. Czyzewski, A. Laskowski, and S. Wasik, “Chessboard and chess piece recognition with the support of neural networks,” Foundations of Computing and Decision Sciences, vol. 45, no. 4, pp. 257–280, 2020. [Online]. Available: https://doi.org/10.2478/fcds-2020-0014

A. de S´ a Delgado Neto and R. Mendes Campello, “Chess position identification using pieces classification based on synthetic images generation and deep neural network fine-tuning,” in 21st Symposium on Virtual and Augmented Reality (SVR), 2019, pp. 152–160. [Online]. Available: https://doi.org/10.1109/SVR.2019.00038

J. Ding, “Chessvision: Chess board and piece recognition,” Stanford University, Tech. Rep., https://web.stanford.edu/class/cs231a/prev_projects_2016/CS_231A_Final_Report.pdf, Accessed: 28/09/2023.

K. Asanovic et al., “The rocket chip generator. eecs department,” University of California, Berkeley, Tech. Rep. UCB/EECS--17, vol. 4, 2016. [Online]. Available: https://github.com/chipsalliance/rocket-chip/tree/47f7b7144727f0340d511d35b9f6c7a91b2a276f

Z. Jerry, B. Korpan, A. Gonzalez, and K. Asanovic, “Sonicboom: The 3rd generation berkeley out-of-order machine,” in Proceedings of the 4th Workshop on Computer Architecture Research with RISC-V (CARRV), pp. 1–7.

Y. Zhou, X. Jin, T. Xiang, and D. Zha, “Enhancing energy efficiency of risc-v processor-based embedded graphics systems through frame buffer compression,” Microprocess. Microsystems, vol. 77, p. 103140, 2020. [Online]. Available: https://doi.org/10.1016/j.micpro.103140

H. Genc et al., “Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration,” in Proceedings of the 58th Annual Design Automation Conference (DAC), 2021, pp. –774. [Online]. Available: https://doi.org/10.1109/DAC18074.2021.9586216

C. Matuszek et al., “Gambit: An autonomous chess-playing robotic system,” in IEEE International Conference on Robotics and Automation, 2011, pp. –4297. [Online]. Available: https://doi.org/10.1109/ICRA.2011.5980528

A. Chen and K. Wang, “Robust computer vision chess analysis and interaction with a humanoid robot,” Computers, vol. 8, p. 14, 02 2019. [Online]. Available: https://doi.org/10.3390/computers8010014

P. Kolosowski, A. Wolniakowski, and K. Miatliuk, “Collaborative robot system for playing chess,” in 2020 International Conference Mechatronic Systems and Materials (MSM), 2020, pp. 1–6. [Online]. Available: https://doi.org/10.1109/MSM49833.2020.9202398

B. Tan, “Towards a vision-based mobile manipulator for autonomous chess gameplay,” Master of Science in Technology Thesis, University of Turku. Department of Computing, Faculty of Technology, Robotics and Autonomous Systems, 2023.

E. Civik and U. Yuzgec, “Real-time driver fatigue detection system with deep learning on a low-cost embedded system,” Microprocessors and Microsystems, vol. 99, p. 104851, 2023. [Online]. Available: https://doi.org/10.1016/j.micpro.2023.104851

J. Mas, T. Panadero, G. Botella, A. A. Del Barrio, and C. Garc´ ıa, “CNN inference acceleration using low-power devices for human monitoring and security scenarios,” Computers & Electrical Engineering, vol. 88, p. 106859, 2020. [Online]. Available: https://doi.org/10.1016/j.compeleceng.2020.106859

Q. Gui, G. Wang, L. Wang, J. Cheng, and H. Fang, “Road surface state recognition using deep convolution network on the low-power-consumption embedded device,” Microprocessors and Microsystems, vol. 96, p. 104740, 2023. [Online]. Available: https://doi.org/10.1016/j.micpro.2022.104740

A. de la Escalera and J. Armingol, “Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration,” Sensors (Basel, Switzerland), vol. 10, pp. 2027–44, 03 2010. [Online]. Available: https://doi.org/10.3390/s100302027

F. Gao, T. Huang, J. Wang, J. Sun, A. Hussain, and E. Yang, “Dual-branch deep convolution neural network for polarimetric sar image classification,” Applied Sciences, vol. 7, p. 447, 04 2017. [Online]. Available: https://doi.org/10.3390/app7050447

A. J. Bency, H. Kwon, H. Lee, S. Karthikeyan, and B. S. Manjunath, “Weakly supervised localization using deep feature maps,” in European Conference on Computer Vision, 2016, pp. 714–731. [Online]. Available: https://doi.org/10.48550/arXiv.1603.00489

D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91–110. [Online]. Available: https://doi.org/10.1023/B:VISI.0000029664.99615.94

Y. Xie, G. Tang, and W. Hoff, “Chess piece recognition using oriented chamfer matching with a comparison to cnn,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 2001–2009. [Online]. Available: https://doi.org/10.1109/WACV.00221

S. J. Edwards, “Portable game notation specification and implementation guide: Forsyth-edwards notation,”

W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Neural Computation, vol. 29, no. 9, pp. 2352–2449, 2017. [Online]. Available: https://doi.org/10.1162/neco_a_00990

F. Chollet et al., “Keras,” https://keras.io, 2015.

M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [Online]. Available: https://doi.org/10.1109/cvpr.2017.243

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size,” 2016. [Online]. Available: https://doi.org/10.48550/arXiv.1602.07360

J. Setoain, M. Prieto, C. Tenllado, A. Plaza, and F. Tirado, “Parallel morphological endmember extraction using commodity graphics hardware,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 3, pp. 441–445, 2007. [Online]. Available: https://doi.org/10.1109/LGRS.2007.897398

J. L. Bentley and T. Ottmann, “Algorithms for reporting and counting geometric intersections,” IEEE Transactions on Computers, vol. C-28, no. 9, pp. 643–647, 1979. [Online]. Available: https://doi.org/10.1109/TC.1979.1675432

A. D¨ orflinger, M. Albers, B. Kleinbeck, Y. Guan, H. Michalik, R. Klink, C. Blochwitz, A. Nechi, and M. Berekovic, “A comparative survey of open-source application-class RISC-V processor implementations,” in Proceedings of the 18th ACM International Conference on Computing Frontiers, ser. CF ’21. Association for Computing Machinery, 2021, pp. –20. [Online]. Available: https://doi.org/10.1145/3458657

W. Li, T. Liu, Z. Xiao, H. Qi, W. Zhu, and J. Wang, “Tcader: A tightly coupled accelerator design framework for heterogeneous system with hard-ware/software co-design,” Journal of Systems Architecture, vol. 136, p. 102822, 2023. [Online]. Available: https://doi.org/10.1016/j.sysarc.2023.102822

Y. Lee, A. Waterman, R. Avizienis, H. Cook, C. Sun, V. Stojanovi´ c, and K. Asanovi´ c, “A nm 1.3ghz 16.7 double-precision gflops/w risc-v processor with vector accelerators,” in 40th European Solid State Circuits Conference (ESSCIRC), pp. 199–202. [Online]. Available: https://doi.org/10.1109/ESSCIRC.2014.6942056

A. Amid et al., “Chipyard: Integrated design, simulation, and implementation framework for custom socs,” IEEE Micro, vol. 40, no. 4, pp. 10–21, 2020. [Online]. Available: <https://doi.org/10.1109/MM.2020.>

C. Danner and M. Kafafy, “Visual chess recog-nition,” http://web.stanford.edu/class/ee368/Project_Spring_1415/Reports/Danner_Kafafy.pdf, 2015.

J. F. Canny, “Finding edges and lines in images,” Theory of Computing Systems - Mathematical Systems Theory, p. 16, 1983.

R. O. Duda and P. E. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Communications of the ACM, vol. 15, no. 1, p. 11–15, [Online]. Available: https://doi.org/10.1145/361242

Most read articles by the same author(s)

1 2 3 4 5 > >>

International Journal of Modern Computer Science and IT Innovations

AFFORDABLE VISION-BASED SYSTEMS FOR REAL-TIME CHESSBOARD DIGITIZATION

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles