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1. Patch-level Gaze Distribution Prediction for Gaze Following

   https://doi.org/10.1109/WACV56688.2023.00094  

   Miao, Qiaomu; Hoai, Minh; Samaras, Dimitris (January 2023, IEEE Winter Conference on Applications of Computer Vision)
2. Serious gaze

   https://doi.org/10.1109/VS-GAMES.2017.8056614  

   Duchowski, Andrew T. (September 2017, 2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games))

   In this paper I review gaze-based interaction, distinguishing eye movement analysis from synthesis in virtual reality and games for serious applications. My focus is on four forms of gaze-based interaction: diagnostic, active, passive, and expressive. In discussing each, I briefly review seminal results and recent advancements, highlighting outstanding research problems. 

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3. Retail Gaze: A Dataset for Gaze Estimation in Retail Environments

   https://doi.org/10.1109/DASA54658.2022.9765224  

   Senarath, Shashimal; Pathirana, Primesh; Meedeniya, Dulani; Jayarathna, Sampath (March 2022, 2022 International Conference on Decision Aid Sciences and Applications (DASA))

   The concept of gaze object estimation predicts a bounding box that a person looks steadily. It is a applicable and contemporary technique in the retail industry. However, the existing datasets for gaze object prediction in retail is limited to controlled environments and do not consider retail product category area segmentation annotations. This paper proposes Retail Gaze, a dataset for gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles. Furthermore, we use state-of-the-art gaze estimation models to benchmark the Retail Gaze dataset and comprehensively analyze the results obtained. 

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4. Gaze-Augmented Drone Navigation

   https://doi.org/10.1145/3582700.3583702  

   Pineda, Kayla; Mahanama, Bhanuka; Ashok, Vikas; Jayarathna, Sampath (March 2023, Proceedings of the Augmented Humans International Conference 2023)

   The use of unmanned aerial vehicles (UAVs) or drones, has significantly increased over the past few years. There is a growing demand in the drone industry, creating new workforce opportunities such as package delivery, search and rescue, real estate, transportation, agriculture, infrastructure inspection, and many others, signifying the importance of effective and efficient control techniques. We propose a scheme for controlling a drone through gaze extracted from eye-trackers, enabling an operator to navigate through a series of waypoints. Then we demonstrate and test the utility of our approach through a pilot study against traditional controls. Our results indicate gaze as a promising control technique for navigating drones revealing novel research avenues. 

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5. Keypoint-Based Gaze Tracking

   https://doi.org/10.1007/978-3-030-68790-8_12  

   Her, Paris; Manderle, Logan; Dias, Philipe A; Medeiros, Henry; Odone, Francesca (February 2021, International Conference on Pattern Recognition)

   null; null; null; null; null; null; null; null (Ed.)

   Effective assisted living environments must be able to perform inferences on how their occupants interact with their environment. Gaze direction provides strong indications of how people interact with their surroundings. In this paper, we propose a gaze tracking method that uses a neural network regressor to estimate gazes from keypoints and integrates them over time using a moving average mechanism. Our gaze regression model uses confidence gated units to handle cases of keypoint occlusion and estimate its own prediction uncertainty. Our temporal approach for gaze tracking incorporates these prediction uncertainties as weights in the moving average scheme. Experimental results on a dataset collected in an assisted living facility demonstrate that our gaze regression network performs on par with a complex, dataset-specific baseline, while its uncertainty predictions are highly correlated with the actual angular error of corresponding estimations. Finally, experiments on videos sequences show that our temporal approach generates more accurate and stable gaze predictions. 

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