arXiv subjects

Seth Schneider

Publications and source records attributed to Seth Schneider.

2 recordsLinked to original sources

arXiv2023

Toward Understanding Display Size for FPS Esports Aiming

Gamers use a variety of different display sizes, though for PC gaming in particular, monitors in the 24 to 27 inch size range have become most popular. Particularly popular among many PC gamers, first person shooter (FPS) games represent a genre where hand-eye coordination is particularly central to the player's performance in game. In a carefully designed pair of experiments on FPS aiming, we compare player performance across a range of display sizes. First, we compare 12.5 inch, 17.3 inch and 24 inch monitors on a multi-target elimination task. Secondly, we highlight the differences between 24.5 inch and 27 inch displays with a small target experiment, specifically designed to amplify these small changes. We find a small, but statistically significant improvement from the larger monitor sizes, which is likely a combined effect between monitor size, resolution, and the player's natural viewing distance.

cs.GR↗

arXiv2021

Robust Vision-Based Cheat Detection in Competitive Gaming

Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the final state of the frame buffer and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation to build a detector that is also resistant to potential adversarial attacks and study its interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.

cs.CV↗