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Enhanced Bot Game for a Massively Multiplayer Online Role-Playing Game (MMORPG) Using Object Detection

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Abstract

The increasing prevalence of the internet has fueled the global expansion of massively multiplayer online role-playing games (MMORPGs), attracting a vast player base. However, players often face time constraints while striving to progress in these immersive virtual worlds. To address this challenge, various techniques have been explored, including Template Matching, Color Segmentation, Canny Edge Detection, and deep learning models, to develop bot games aimed at assisting players. In this study, we evaluated the effectiveness of several object detection techniques, namely Template Matching, Color Segmentation, Canny Edge Detection, Cascade Classifier, and YOLOv5, within the context of MMORPGs, focusing on the Dragonica game. Through experimentation and analysis, we found that YOLOv5 outperformed other techniques in terms of object detection accuracy and efficiency. While Template Matching exhibited proficiency in detecting stationary objects with consistent attributes, it proved inadequate for dynamic environments characterized by moving objects. Our findings underscore the importance of comprehensive model training across diverse game scenarios and the incorporation of instance object detection to enhance precision. Additionally, we highlight the need for further research to optimize object detection methodologies in MMORPGs, aiming for robust and generalized solutions to cater to the evolving dynamics of virtual worlds. Overall, this study provides insights into the efficacy of different object detection techniques in MMORPGs, offering a roadmap for future advancements in this domain.

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Data Availability

The image datasets used to conduct the current study are manually captured from the Dragonica game referred as [6] in this paper.

References

  1. Aspat A, Lemos E, Ghoshal A (2021) Stone, paper, scissors mini-game for AI pet robot. Int J Appl Sci Smart Technol 03(01):55–64. https://doi.org/10.24071/ijasst.v3i1.2754

  2. Balakrishnan A, Ramana K, Dhiman G, Ashok G, Bhaskar V, Sharma A, Gaba GS, Masud M, Al-Amri JF (2021) Multimedia concepts on object detection and recognition with F1 car simulation using convolutional layers. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/5543720

  3. Bradski G, Kaehler A (2008) Template matching. learning OpenCV (4.6.0-dev): Computer vision with the OpenCV library.

  4. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;6:679–98.

    Article  Google Scholar 

  5. Dharmawan T, Hanafiah N. Clicker bot for Gacha games using image recognition. Proc Comput Sci. 2021;179(2020):598–605. https://doi.org/10.1016/j.procs.2021.01.045.

    Article  Google Scholar 

  6. Dragonica Online (EMP) | Patch: 10.9.5.3 (Client Ver : 2.0.101)

  7. Joblove GH, Greenberg D. Color spaces for computer graphics. Comput Graph. 1978;12:20–7. https://doi.org/10.1145/965139.807362.

    Article  Google Scholar 

  8. Jocher G et al. (2021) ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo. https://doi.org/10.5281/zenodo.5563715

  9. Lohokare A, Shah A, Zyda M (2020) Deep learning bot for league of legends. In: Proceedings of the 16th AAAI conference on artificial intelligence and interactive digital entertainment, AIIDE 2020, 322–324.

  10. Maida J (2022) MMO Games Market: Segmentation by revenue model (F2P and P2P), genre (MMORPG, MMOFPS, MMORTS, and Others), and geography (APAC, North America, Europe, South America, and MEA)--Forecast till 2025|Technavio. Technavio Research. https://finance.yahoo.com/news/mmo-games-market-segmentation-revenue-100000565.html

  11. Motupalli C, Mohinth R, Gaur S, Mittal V, Prakash S. Supervision of video game car steering implementing HORCNN network. Data Sci Eng (Confluence). 2022;2022:291–4. https://doi.org/10.1109/Confluence52989.2022.9734198.

    Article  Google Scholar 

  12. Nepal U, Eslamiat H (2022) Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs. Sensors, 22(2). https://doi.org/10.3390/s22020464

  13. Ramya A, Gupta Pola V, Bhavya Vaishnavi A, Suraj Karra S (2021) Comparison of YOLOv3, YOLOv4 and yolov5 performance for detection of blood cells. International Research Journal of Engineering and Technology, 4225–4229. www.irjet.net

  14. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2016, pp. 779–788. https://doi.org/10.1109/cvpr.2016.91.

  15. Rickard M (2021) OSRSNet: real-time object recognition in 3D MMORPGS. Stanford Graduate School of Business. p. 1–6. https://cs230.stanford.edu/projects_winter_2021/reports/70280823.pdf.

  16. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. https://doi.org/10.1109/cvpr.2001.990517

  17. Feng C, Cao Z, Xiao Y, Fang Z. Joey Tianyi Zhou: multi-spectral template matching based object detection in a few-shot learning manner. Inf Sci. 2023;624:20–36.

    Article  Google Scholar 

  18. Kerdvibulvech C, Saito H Markerless guitarist fingertip detection using a bayesian classifier and a template matching for supporting guitarists. In: Proc. 10th Virtual Reality Int. Conf., April 2008.

  19. Aoyagi S, Hattori N, Kohama A, Komai S, Suzuki M, Takano M, Fukui E. Object detection and recognition using template matching with SIFT features assisted by invisible floor marks. J Robot Mech. 2009;21(6):689–97.

    Article  Google Scholar 

  20. Mahmoud H. Annaby, Yasser Fouda: fast template matching and object detection techniques using φ-correlation and binary circuits. Multim Tools Appl. 2024;83(3):6469–96.

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Applied Statistics, National Institute of Development Administration, 148 Serithai Road, Klong-Chan, Bangkapi, Bangkok, 10240, Thailand

    Manadda Jaruschaimongkol, Chayakan Chanchai, Sarawut Kotrcha & Thitirat Siriborvornratanakul

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  1. Manadda Jaruschaimongkol
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  2. Chayakan Chanchai
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  3. Sarawut Kotrcha
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The authors contributed equally to this work.

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Correspondence to Thitirat Siriborvornratanakul.

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Jaruschaimongkol, M., Chanchai, C., Kotrcha, S. et al. Enhanced Bot Game for a Massively Multiplayer Online Role-Playing Game (MMORPG) Using Object Detection. SN COMPUT. SCI. 5, 845 (2024). https://doi.org/10.1007/s42979-024-03157-w

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