Unterschiede
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Beide Seiten der vorigen Revision Vorhergehende Überarbeitung Nächste Überarbeitung | Vorhergehende Überarbeitung | ||
arbeiten:latencycsgo [27.04.2021 20:28] – 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 Artificial Neural Networks | + | Thema : Development and Evaluation of Ingame Latency Compensation Technique |
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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]. |
- | • Time-offsetting techniques enable a rollback to the previous state of the game [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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=== Zielsetzung der Arbeit === | === Zielsetzung der Arbeit === | ||
- | Firstly a suitable way to collect data from CS:GO needs to be implemented. There a two possible ways to collect data: (1) Directly log user input and game screen while live-playing and (2) use openly availabe CS:GO replays. Both approaches need to be evaluated and analysed | + | Firstly a suitable way to collect data from CS:GO needs to be implemented. There a two possible ways to collect data: (1) Directly log user input and game screen while live-playing and (2) use openly availabe CS:GO replays. Both approaches need to be evaluated and analyzed |
After collecting a suitable data set from either one of the above options the ANN needs to be developed. The ANNs prediciton is based on the previously gathered data and ultimately should be able to infere the users next inputs. | After collecting a suitable data set from either one of the above options the ANN needs to be developed. The ANNs prediciton is based on the previously gathered data and ultimately should be able to infere the users next inputs. | ||
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[1] Latency. (2021). Wikipedia. https:// | [1] Latency. (2021). Wikipedia. https:// | ||
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[2] Claypool, M., & Claypool, K. (2006). Latency and player actions in online games. Communications of the ACM, 49(11), 40-45. | [2] Claypool, M., & Claypool, K. (2006). Latency and player actions in online games. Communications of the ACM, 49(11), 40-45. | ||
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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. | + | |
- | [5] Sabet, S. S., Schmidt, S., Zadtootaghaj, | + | [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. |
- | [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. | + | |
- | [7] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. | + | [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, | ||
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+ | [7] Valentin Schwind, David Halbhuber, Jakob Fehle, Jonathan Sasse, Andreas Pfaffelhuber, | ||
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+ | [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. | ||
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+ | [9] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. | ||