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arbeiten:latencycsgo [27.04.2021 20:43] – [Data-Entry] David Halbhuberarbeiten:latencycsgo [24.01.2022 11:10] (aktuell) – [Data-Entry] David Halbhuber
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-====== Development and Evaluation of a Ingame Latency Compensation Technique based on Artificial Neural Networks ======+====== Development and Evaluation of Ingame Latency Compensation Technique based on Artificial Neural Networks ======
  
 ---- dataentry StudentischeArbeit ---- ---- dataentry StudentischeArbeit ----
-Thema                                  : Development and Evaluation of a Ingame Latency Compensation Technique based on Artificial Neural Networks #  +Thema                                  : Development and Evaluation of Ingame Latency Compensation Technique based on Artificial Neural Networks 
-Art_thesistypes                        : BA #  +Art_thesistypes                        : BA 
-BetreuerIn_thesisadvisor               : David Halbhuber #  +BetreuerIn_thesisadvisor               : David Halbhuber 
-BearbeiterIn                           : Julian Hoepfinger #  +BearbeiterIn                           : Julian Hoepfinger 
-ErstgutachterIn_thesisprofessor        :  #  +ErstgutachterIn_thesisprofessor        : Niels Henze 
-ZweitgutachterIn_secondthesisprofessor :  #  +ZweitgutachterIn_secondthesisprofessor : Valentin Schwind 
-Status_thesisstate                     : Entwurf #  +Status_thesisstate                     : abgeschlossen 
-Stichworte_thesiskeywords              : Counter Strike, Latency, ANN #  +Stichworte_thesiskeywords              : Counter Strike, Latency, ANN 
-angelegt_dt                            : 2021-04-27 #  +angelegt_dt                            : 2021-04-27 
-Anmeldung_dt                           :  #  +Anmeldung_dt                           :  
-Antrittsvortrag_dt                     :  #  +Antrittsvortrag_dt                     : 2021-07-12 
-Abschlussvortrag_dt                    :  #  +Abschlussvortrag_dt                    :  
-Abgabe_dt                              :  # +Abgabe_dt                              : 
 Textlizenz_textlicense                 :  # #Lizenz|## Textlizenz_textlicense                 :  # #Lizenz|##
 Codelizenz_codelicense                 :  # #Lizenz|## Codelizenz_codelicense                 :  # #Lizenz|##
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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 with ANNs is their long runtime due to internal complexity [7]. If used for latency compensation in video games, the ANNs inference needs to be fast and lightweight. If inference takes too long an ANN based latency compensation technique ultimately leads to an increase in overall latency.+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 of ANNs is their long runtime due to internal complexity [9]. If used for latency compensation in video games, the ANNs inference needs to be fast and lightweight. If inference takes too long an ANN based latency compensation technique ultimately leads to an increase in overall latency.
  
 This work aims to build and evaluate a predictive system to decrease perceived latency for CS:GO.  This work aims to build and evaluate a predictive system to decrease perceived latency for CS:GO. 
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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. 
 + 
 +[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. 
 + 
 +[6] 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 of delay on cloud gaming quality of experience. In Proceedings of the 11th ACM Multimedia Systems Conference (pp. 15-25).
  
-[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 of delay on cloud gaming quality of experience. In Proceedings of the 11th ACM Multimedia Systems Conference (pp. 15-25).+[7] Valentin Schwind, David Halbhuber, Jakob Fehle, Jonathan Sasse, Andreas Pfaffelhuber, Christoph Tögel, Julian Dietz, and Niels Henze. 2020. The Effects of Full-Body Avatar Movement Predictions in Virtual Reality using Neural Networks. In 26th ACM Symposium on Virtual Reality Software and Technology. 1–11.
  
-[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.