This paper studies the use of eye tracking in a First-Person Shooter (FPS) game as a mechanism to: (1) control the attention of the player’s avatar according to the attention deployed by the player; and (2) guide the gameplay and game’s procedural content generation, accordingly. This results in a more natural use of eye tracking in comparison to a use in which the eye tracker directly substitutes control input devices, such as gamepads. The study was conducted on a custom endless runner FPS, Zombie Runner, using an affordable eye tracker. Evaluation sessions showed that the proposed use of eye tracking provides a more challenging and immersive experience to the player, when compared to its absence. However, a strong correlation between eye tracker calibration problems and player’s overall experience was found. This means that eye tracking technology still needs to evolve but also means that once technology gets mature enough players are expected to benefit greatly from the inclusion of eye tracking in their gaming experience.
Within the EDPCG framework, it is possible to generate levels that are adapted to the strengths and limitations of the player, in an attempt to maximize the fun factor. Many studies propose that the fun and challenge factors are directly linked @cite_29 @cite_18 @cite_9 @cite_10 @cite_0 @cite_25 @cite_8 , which means that, to tune the fun factor, one often has to tune the challenge factor. In this line, in this paper, we propose the integration of PGC with gaze tracking so as to adapt the challenge the player as to face according to his her form of playing and, as a result , to improve the fun factor.
@cite_18 @cite_8 @cite_9 @cite_29 @cite_0 @cite_10 @cite_25
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Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels. Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of @math allows the agent to repeat a selected action @math number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest. This paper proposes a novel high-accuracy stereo matching scheme based on adaptive ground control points (AdaptGCP). Different from traditional fixed GCP-based methods, we consider color dissimilarity, spatial relation, and the pixel-matching reliability to select GCP adaptively in each local support window. To minimize the global energy, we propose a practical solution, named as alternating updating scheme of disparity and confidence map, which can effectively eliminate the redundant and interfering information of unreliable pixels. The disparity values of those unreliable pixels are reassigned with the information provided by local plane model, which is fitted with GCPs. Then, the confidence map is updated according to the disparity reassignment and the left—right consistency. Finally, the disparity map is refined by multistep filers. Quantitative evaluations demonstrate the effectiveness of our AdaptGCP scheme for regularizing the ill-posed matching problem. The top ranks on Middlebury benchmark with different error thresholds show that our algorithm achieves the state-of-the-art performance among the latest stereo matching algorithms. This paper provides a new insight toward high-accuracy stereo matching. This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77 and 88.66 respectively) via a simple single-neuron model whereas model accuracy for fun (69.18 ) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.