Evaluation of the Prospectivity of a Hybrid Model Modeling of Visual Perception by Game Agents using Texture Drawing
Main Article Content
Abstract
This article presents a newly developed visual perception algorithm for game agents, implemented in the Unity game engine. The proposed method is based on comparing images from two cameras, taking into account complex visual effects (lighting, shadows, occlusion), and is supplemented by line-of-sight checks, consideration of object velocity, and gradual detection mechanics. The algorithm was optimized using asynchronous computations, dynamic camera activation, and accelerated algorithms. The system was tested under various load levels, and conclusions were drawn regarding the optimal conditions for the algorithm’s operation. The paper also analyzes the scientific literature on similar solutions, identifying their strengths and weaknesses. The results can be applied in video game development to create realistic behavior for non-player characters, especially in games with stealth elements.
Keywords:
Article Details
References
2. Mahmoud I., Jaffal Y., Wloka D. A Vision Simulation Algorithm for Non-Player Character in Static Scene // University of Kassel, Germany Kassel. 2014. P. 1–6.
3. Vehkala M. Creating the AI for the Living, Breathing World of Hitman: Absolution. GDC 2013. URL: https://www.gdcvault.com/play/1019353
4. McIntosh T. The Last of Us: Human Enemy AI. GDC 2014. URL: https://www.gdcvault.com/play/1020338/The-Last-of-Us-Human
5. Walsh M. Modeling AI Perception and Awareness in Splinter Cell: Blacklist. GDC 2014. URL: https://youtu.be/RFWrKHM0vAg
6. McIntosh T. Human Enemy AI in The Last of Us. In Game AI Pro 360. CRC Press. 2019. P. 13–24.
7. Welsh, R. Crytek’s Target Tracks Perception System. In Game AI Pro: Collected Wisdom of Game AI Professionals, CRC Press. 2013. P. 403–411.
8. Walsh M. Modeling Perception and Awareness in Tom Clancy’s Splinter Cell Blacklist // In Game AI Pro 360, CRC Press. 2019. P. 73–86.
9. Ying Z., Edwards N., Kutuzov M. Efficient Visibility Approximation for Game AI using Neural Omnidirectional Distance Fields // Proceedings of the ACM on Computer Graphics and Interactive Techniques. 2024. Vol. 7, No. 1. P. 1–15.
10. Estgren M. Modelling NPC perception using supervised learning. Uppsala University, Sweden Uppsala, 2021. 8 p.
11. Bourg D.M., Seemann G. AI for Game Developers. O’Reilly Media, Inc. 2004. 371 p.
12. Примаченко А.М., Хафизов М.Р. Разработка системы визуального восприятия игровых агентов в видеоиграх // Электронные библиотеки. 2025. Т. 28, № 3. C. 506–531.
13. NPC Eyes Sight System – PRO. URL: https://www.fab.com/listings/6b54716a-dd21-414d-b78f-384068de14b7
14. Erdelyi C. Using Computer Vision Techniques to Play an Existing Video Game. California State University San Marcos, San Marcos, 2019. 49 p.
15. Pramod R.T., Katti H., Arun S.P. Human peripheral blur is optimal for object recognition // Vision Research. 2022. Vol. 200. No. 108083.
16. Jarosz W. Fast Image Convolutions // ACM SIGGRAPH University of Illinois Urbana-Champaign. 2001. 11 p.
17. Fastest Gaussian Blur (in Linear Time). URL: https://blog.ivank.net/fastest-gaussian-blur.html
This work is licensed under a Creative Commons Attribution 4.0 International License.
Presenting an article for publication in the Russian Digital Libraries Journal (RDLJ), the authors automatically give consent to grant a limited license to use the materials of the Kazan (Volga) Federal University (KFU) (of course, only if the article is accepted for publication). This means that KFU has the right to publish an article in the next issue of the journal (on the website or in printed form), as well as to reprint this article in the archives of RDLJ CDs or to include in a particular information system or database, produced by KFU.
All copyrighted materials are placed in RDLJ with the consent of the authors. In the event that any of the authors have objected to its publication of materials on this site, the material can be removed, subject to notification to the Editor in writing.
Documents published in RDLJ are protected by copyright and all rights are reserved by the authors. Authors independently monitor compliance with their rights to reproduce or translate their papers published in the journal. If the material is published in RDLJ, reprinted with permission by another publisher or translated into another language, a reference to the original publication.
By submitting an article for publication in RDLJ, authors should take into account that the publication on the Internet, on the one hand, provide unique opportunities for access to their content, but on the other hand, are a new form of information exchange in the global information society where authors and publishers is not always provided with protection against unauthorized copying or other use of materials protected by copyright.
RDLJ is copyrighted. When using materials from the log must indicate the URL: index.phtml page = elbib / rus / journal?. Any change, addition or editing of the author's text are not allowed. Copying individual fragments of articles from the journal is allowed for distribute, remix, adapt, and build upon article, even commercially, as long as they credit that article for the original creation.
Request for the right to reproduce or use any of the materials published in RDLJ should be addressed to the Editor-in-Chief A.M. Elizarov at the following address: amelizarov@gmail.com.
The publishers of RDLJ is not responsible for the view, set out in the published opinion articles.
We suggest the authors of articles downloaded from this page, sign it and send it to the journal publisher's address by e-mail scan copyright agreements on the transfer of non-exclusive rights to use the work.