A comparison of head and manual control for a position-control pursuit tracking task

Head control was compared with manual control in a pursuit tracking task involving proportional controlled-element dynamics. An integrated control/display system was used to explore tracking effectiveness in horizontal and vertical axes tracked singly and concurrently. Compared with manual tracking, head tracking resulted in a 50 percent greater rms error score, lower pilot gain, greater high-frequency phase lag and greater low-frequency remnant. These differences were statistically significant, but differences between horizontal- and vertical-axis tracking and between 1- and 2-axis tracking were generally small and not highly significant. Manual tracking results were matched with the optimal control model using pilot-related parameters typical of those found in previous manual control studies. Head tracking performance was predicted with good accuracy using the manual tracking model plus a model for head/neck response dynamics obtained from the literature

Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported

Head tracking at large angles from the straight ahead position

One of the big advantages of a helmet sight in a high performance aircraft is its off-boresight capability in aiming a fire control system. However, tracking data using a target that is moving rapidly and randomly for an extended period of time is missing. This study is intended to provide data in this area that will be of value to engineers in designing head control systems

A stabilized adaptive appearance changes model for 3D head tracking

A simple method is presented for 3D head pose estimation and tracking in monocular image sequences. A generic geometric model is used. The initialization consists of aligning the perspective projection of the geometric model with the subjects head in the initial image. After the initialization, the gray levels from the initial image are mapped onto the visible side of the head model to form a textured object. Only a limited number of points on the object is used allowing real-time performance even on low-end computers. The appearance changes caused by movement in the complex light conditions of a real scene present a big problem for fitting the textured model to the data from new images. Having in mind real human-computer interfaces we propose a simple adaptive appearance changes model that is updated by the measurements from the new images. To stabilize the model we constrain it to some neighborhood of the initial gray values. The neighborhood is defined using some simple heuristic

Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm

3D head tracking using normal flow constraints in a vehicle environment

Head tracking is a key component in applications such as human computer interaction, person monitoring, driver monitoring, video conferencing, and object-based compression. The motion of a driver’s head can tell us a lot about his/her mental state; e.g. whether he/she is drowsy, alert, aggressive, comfortable, tense, distracted, etc. This paper reviews an optical flow based method to track the head pose, both orientation and position, of a person and presents results from real world data recorded in a car environment

Investigation of Rhine Pointing as a Solution to the Aircraft Human Machine Interface Problem

The human machine interface in 5th generation aircraft has not evolved proportionally with advances in display size and data density. The traditional cursor slew method fails to rapidly relocate the cursor, especially on large displays. Previous studies at the Air Force Test Pilot School and Air Force Institute of Technology identified methods that have the potential to improve the human machine interface. This research expanded upon those studies by providing an assessment of head tracking technology as a secondary method of cursor manipulation. Specifically, this study examined the effects that visual feedback (visible and invisible head tracking cursors) and cursor configurations (Z-Axis and X/Y-Axis snap button) had on performance in a target selection task. A Fitts Law regression was conducted to fit the collected data to a predictive model, but this was unsuccessful. Dependent variables such as time to initiate head tracking snap, accuracy of head tracking snap, and total time to select target were examined to compare the different configurations. After initial data analysis was complete, an assessment of learning effects was conducted. The initial data analysis found all head tracking configurations to be faster than the traditional cursor slew method. Visible conditions were consistently more accurate and had lower total selection times than the invisible conditions. Invisible conditions had faster times to initiate the cursor snap, indicating that the participants were not attempting to make fine adjustments with the head tracking cursor. There were no observed learning effects in this study. The resulting conclusions are discussed and recommendations for future research are proposed including study of fatigue in the target selection task, target selection as a secondary task, and the combination of rhino pointing with eye tracking capabilities

Head-Tracking Wireless Streaming Device

In various businesses and services, there is a need for tight integration between visual media, human response to that media, and coordination of that response. For example, emergency responders may need information from a separate perspective using robotically controlled cameras in order to improve coordination efforts. The aim of this project is to design a low cost, high performance video streaming device. The essential feature of our design is to wirelessly send a video stream from a webcam to a micro-display and remotely control the orientation of the webcam using head movements. There are many future applications for this project including target recognition, blind spot detection, robotics, human studies, and security. Future improvements include the utilization of a transparent screen using OLED technology, power aware computing, data overlay onto the image displayed to the user, and a more ergonomic electronic solution.https://scholarscompass.vcu.edu/capstone/1042/thumbnail.jp