A Real-Time Video-based Eye Tracking Approach for Driver Attention Study

nowing the driver's point of gaze has significant potential to enhance driving safety, eye movements can be used as an indicator of the attention state of a driver; but the primary obstacle of integrating eye gaze into today's large scale real world driving attention study is the availability of a reliable, low-cost eye-tracking system. In this paper, we make an attempt to investigate such a real-time system to collect driver's eye gaze in real world driving environment. A novel eye-tracking approach is proposed based on low cost head mounted eye tracker. Our approach detects corneal reflection and pupil edge points firstly, and then fits the points with ellipse. The proposed approach is available in different illumination and driving environment from simple inexpensive head mounted eye tracker, which can be widely used in large scale experiments. The experimental results illustrate our approach can reliably estimate eye position with an accuracy of average 0.34 degree of visual angle in door experiment and 2--5 degrees in real driving environments

Review on Augmented Reality in Oral and Cranio-Maxillofacial Surgery: Toward 'Surgery-Specific' Head-Up Displays

In recent years, there has been an increasing interest towards the augmented reality as applied to the surgical field. We conducted a systematic review of literature classifying the augmented reality applications in oral and cranio-maxillofacial surgery (OCMS) in order to pave the way to future solutions that may ease the adoption of AR guidance in surgical practice. Publications containing the terms 'augmented reality' AND 'maxillofacial surgery', and the terms 'augmented reality' AND 'oral surgery' were searched in the PubMed database. Through the selected studies, we performed a preliminary breakdown according to general aspects, such as surgical subspecialty, year of publication and country of research; then, a more specific breakdown was provided according to technical features of AR-based devices, such as virtual data source, visualization processing mode, tracking mode, registration technique and AR display type. The systematic search identified 30 eligible publications. Most studies (14) were in orthognatic surgery, the minority (2) concerned traumatology, while 6 studies were in oncology and 8 in general OCMS. In 8 of 30 studies the AR systems were based on a head-mounted approach using smart glasses or headsets. In most of these cases (7), a video-see-through mode was implemented, while only 1 study described an optical-see-through mode. In the remaining 22 studies, the AR content was displayed on 2D displays (10), full-parallax 3D displays (6) and projectors (5). In 1 case the AR display type is not specified. AR applications are of increasing interest and adoption in oral and cranio-maxillofacial surgery, however, the quality of the AR experience represents the key requisite for a successful result. Widespread use of AR systems in the operating room may be encouraged by the availability of 'surgery-specific' head-mounted devices that should guarantee the accuracy required for surgical tasks and the optimal ergonomics

3D head motion, point-of-regard and encoded gaze fixations in real scenes: next-generation portable video-based monocular eye tracking

Portable eye trackers allow us to see where a subject is looking when performing a natural task with free head and body movements. These eye trackers include headgear containing a camera directed at one of the subject\u27s eyes (the eye camera) and another camera (the scene camera) positioned above the same eye directed along the subject\u27s line-of-sight. The output video includes the scene video with a crosshair depicting where the subject is looking -- the point-of-regard (POR) -- that is updated for each frame. This video may be the desired final result or it may be further analyzed to obtain more specific information about the subject\u27s visual strategies. A list of the calculated POR positions in the scene video can also be analyzed. The goals of this project are to expand the information that we can obtain from a portable video-based monocular eye tracker and to minimize the amount of user interaction required to obtain and analyze this information. This work includes offline processing of both the eye and scene videos to obtain robust 2D PORs in scene video frames, identify gaze fixations from these PORs, obtain 3D head motion and ray trace fixations through volumes-of-interest (VOIs) to determine what is being fixated, when and where (3D POR). To avoid the redundancy of ray tracing a 2D POR in every video frame and to group these POR data meaningfully, a fixation-identification algorithm is employed to simplify the long list of 2D POR data into gaze fixations. In order to ray trace these fixations, the 3D motion -- position and orientation over time -- of the scene camera is computed. This camera motion is determined via an iterative structure and motion recovery algorithm that requires a calibrated camera and knowledge of the 3D location of at least four points in the scene (that can be selected from premeasured VOI vertices). The subjects 3D head motion is obtained directly from this camera motion. For the final stage of the algorithm, the 3D locations and dimensions of VOIs in the scene are required. This VOI information in world coordinates is converted to camera coordinates for ray tracing. A representative 2D POR position for each fixation is converted from image coordinates to the same camera coordinate system. Then, a ray is traced from the camera center through this position to determine which (if any) VOI is being fixated and where it is being fixated -- the 3D POR in the world. Results are presented for various real scenes. Novel visualizations of portable eye tracker data created using the results of our algorithm are also presented

Study of Human Hand-Eye Coordination Using Machine Learning Techniques in a Virtual Reality Setup

Theories of visually guided action are characterized as closed-loop control in the presence of reliable sources of visual information, and predictive control to compensate for visuomotor delay and temporary occlusion. However, prediction is not well understood. To investigate, a series of studies was designed to characterize the role of predictive strategies in humans as they perform visually guided actions, and to guide the development of computational models that capture these strategies. During data collection, subjects were immersed in a virtual reality (VR) system and were tasked with using a paddle to intercept a virtual ball. To force subjects into a predictive mode of control, the ball was occluded or made invisible for a portion of its 3D parabolic trajectory. The subjects gaze, hand and head movements were recorded during the performance. To improve the quality of gaze estimation, new algorithms were developed for the measurement and calibration of spatial and temporal errors of an eye tracking system. The analysis focused on the subjects gaze and hand movements reveal that, when the temporal constraints of the task did not allow the subjects to use closed-loop control, they utilized a short-term predictive strategy. Insights gained through behavioral analysis were formalized into computational models of visual prediction using machine learning techniques. In one study, LSTM recurrent neural networks were utilized to explain how information is integrated and used to guide predictive movement of the hand and eyes. In a subsequent study, subject data was used to train an inverse reinforcement learning (IRL) model that captures the full spectrum of strategies from closed-loop to predictive control of gaze and paddle placement. A comparison of recovered reward values between occlusion and no-occlusion conditions revealed a transition from online to predictive control strategies within a single course of action. This work has shed new insights on predictive strategies that guide our eye and hand movements

On Inter-referential Awareness in Collaborative Augmented Reality

For successful collaboration to occur, a workspace must support inter-referential awareness - or the ability for one participant to refer to a set of artifacts in the environment, and for that reference to be correctly interpreted by others. While referring to objects in our everyday environment is a straight-forward task, the non-tangible nature of digital artifacts presents us with new interaction challenges. Augmented reality (AR) is inextricably linked to the physical world, and it is natural to believe that the re-integration of physical artifacts into the workspace makes referencing tasks easier; however, we find that these environments combine the referencing challenges from several computing disciplines, which compound across scenarios. This dissertation presents our studies of this form of awareness in collaborative AR environments. It stems from our research in developing mixed reality environments for molecular modeling, where we explored spatial and multi-modal referencing techniques. To encapsulate the myriad of factors found in collaborative AR, we present a generic, theoretical framework and apply it to analyze this domain. Because referencing is a very human-centric activity, we present the results of an exploratory study which examines the behaviors of participants and how they generate references to physical and virtual content in co-located and remote scenarios; we found that participants refer to content using physical and virtual techniques, and that shared video is highly effective in disambiguating references in remote environments. By implementing user feedback from this study, a follow-up study explores how the environment can passively support referencing, where we discovered the role that virtual referencing plays during collaboration. A third study was conducted in order to better understand the effectiveness of giving and interpreting references using a virtual pointer; the results suggest the need for participants to be parallel with the arrow vector (strengthening the argument for shared viewpoints), as well as the importance of shadows in non-stereoscopic environments. Our contributions include a framework for analyzing the domain of inter-referential awareness, the development of novel referencing techniques, the presentation and analysis of our findings from multiple user studies, and a set of guidelines to help designers support this form of awareness

Optical Guidance System /OGS/ for rendezvous and docking Final report

Optical guidance system for Apollo rendezvous and dockin

Predicting human behavior in smart environments: theory and application to gaze prediction

Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors

Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models