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arbeiten:latencycsgo [27.04.2021 20:31] – [Data-Entry] David Halbhuber | arbeiten:latencycsgo [24.01.2022 11:10] (aktuell) – [Data-Entry] David Halbhuber | ||
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- | ====== Development and Evaluation of a Ingame Latency Compensation Technique Artificial Neural Networks ====== | + | ====== Development and Evaluation of Ingame Latency Compensation Technique |
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- | Thema : Development and Evaluation of a Ingame Latency Compensation Technique based Artificial Neural Networks | + | Thema : Development and Evaluation of Ingame Latency Compensation Technique based on Artificial Neural Networks |
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- | ZweitgutachterIn_secondthesisprofessor : | + | ZweitgutachterIn_secondthesisprofessor : Valentin Schwind |
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=== Hintergrund === | === Hintergrund === | ||
- | “Latency from a general point of view is a time delay between the cause and the effect of some physical change in the system being observed” [1]. Latency in video games impairs the performance of the player and game experience. The performance in games, which require precision and have tight deadlines | + | “Latency from a general point of view is a time delay between the cause and the effect of some physical change in the system being observed” [1]. Latency in video games impairs the performance of the player and game experience. The performance in fast-paced |
* Delayed input techniques try to add delay to local actions to allow simultaneous execution by all clients [4, 5]. | * Delayed input techniques try to add delay to local actions to allow simultaneous execution by all clients [4, 5]. | ||
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* Predictive techniques estimate the occurring events in the game from the locally available state [4, 5]. | * Predictive techniques estimate the occurring events in the game from the locally available state [4, 5]. | ||
- | The goal of this work is to build a predictive technique for CS:GO using artificial neural networks (ANN). ANNs have been successfully used in computer vision tasks, such as pattern recognition in images [6]. One problem | + | The goal of this work is to build a predictive technique for CS:GO using artificial neural networks (ANN). ANNs have been successfully used in computer vision tasks, such as pattern recognition in images [8], to predict avatar movement in VR [7] or reduce perceived input latency on touch devices [4]. However, one problem |
This work aims to build and evaluate a predictive system to decrease perceived latency for CS: | This work aims to build and evaluate a predictive system to decrease perceived latency for CS: | ||
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[3] Claypool, M., & Claypool, K. (2010, February). Latency can kill: precision and deadline in online games. In Proceedings of the first annual ACM SIGMM conference on Multimedia systems (pp. 215-222). | [3] Claypool, M., & Claypool, K. (2010, February). Latency can kill: precision and deadline in online games. In Proceedings of the first annual ACM SIGMM conference on Multimedia systems (pp. 215-222). | ||
- | [4] Savery, C., & Graham, T. N. (2013). Timelines: simplifying the programming of lag compensation for the next generation of networked games. Multimedia Systems, 19(3), 271-287. | + | [4] Niels Henze, Markus Funk, and Alireza Sahami Shirazi. 2016. Software-reduced touchscreen latency. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. 434–441. |
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+ | [5] Savery, C., & Graham, T. N. (2013). Timelines: simplifying the programming of lag compensation for the next generation of networked games. Multimedia Systems, 19(3), 271-287. | ||
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+ | [6] Sabet, S. S., Schmidt, S., Zadtootaghaj, | ||
- | [5] Sabet, S. S., Schmidt, S., Zadtootaghaj, S., Naderi, B., Griwodz, C., & Möller, S. (2020, May). A latency compensation technique based on game characteristics to mitigate the influence | + | [7] Valentin Schwind, David Halbhuber, Jakob Fehle, Jonathan Sasse, Andreas Pfaffelhuber, Christoph Tögel, Julian Dietz, and Niels Henze. 2020. The Effects |
- | [6] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee. | + | [8] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee. |
- | [7] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. | + | [9] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. |