# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 2 Related Works
### Eye-Gaze Tracking  
The eye-gaze tracking technology has matured and become inexpensive. This technology can be built into computers, HMDs, and mobile devices [8]. The direction of the eye-gaze is a crucial and augmented input medium/control modality through which humans express socially relevant information, particularly the individual's cognitive state [7; 20; 21]. Hence, various studies on gaze tracking have been conducted beginning with a video-based eye tracking study on a pilot-operated airplane [22]. The state-of-the-art eye-tracking devices can be divided into two main categories: table-mounted and mobile (head-mounted) eye-tracking systems, while two-directional (2D) appearance-based and 3D model-based techniques are the two main methods for gaze estimation [23; 24; 25].  
Gaze tracking has been advanced with the goal of improving accuracy and reducing the constraints on the users [26]. Active advancements in the speed of computation, digital video processing, and low-cost hardware have relatively increased the accessibility of gaze-tracking devices to both expert and novice users. Therefore, gaze tracking has found applications in gaming, interactive applications, diagnostics, rehabilitation, desktop computers, automotive setups, TV panels, and virtual and augmented reality applications [27; 28; 29; 30; 31; 32]. These various applications and eye-gaze research include studies on several types of eye movements, as given in Table 1, to collect information about a user's intent, behavior, cognitive state, and attention [33].  
While all of these applications use one or another eye-tracking devices, the physiology of the human eye is not uniform throughout the population. The eye tracker has a job at hand to calibrate itself or be calibrated manually before use according to the user's physiology in order to achieve the desired results. It goes without saying that calibration is an intrinsic phenomenon of any eye-tracking system [34; 35]. Most eye-movement researchers use algorithms to parse raw data and differentiate among the various types of eye movements (Table 1); however, the process of identifying and separating fixations and saccades in eye tracking remains an essential part of an eye-movement data analysis. [PERSON] et al. [36] discussed and evaluated various algorithms such as velocity-threshold identification (I-VT), hidden Markov model fixation identification (I-HMM), dispersion-threshold identification (I-DT), minimum spanning tree identification (I-MST), and area-of-interest fixation identification (I-AOI) for automatic fixation and saccade identification. A qualitative analysis of these algorithms summarized that I-DT provides accurate and robust fixation identification. These automatic identification algorithms enable eye trackers for real-time adoptions [37]. In the proposed GazeGuide, we used I-DT for filtering the raw gaze data from the eye tracker into fixations in real time.  
During the past several decades, there has been a dramatic increase in the quantity and variety of studies using the eye-tracking technology. Figure 1 illustrates the number of peer-reviewed articles over the past 55 years (merged into successive five-year periods) containing the phrase \"eye tracking\" and/or \"eye movements.\"