A robust head pose tracking system based on multiple cameras (2025)

Vision-based System for Head Pose Tracking in Indoor Immersive Environments

We describe an inexpensive, accurate, fast and scalable vision based indoor head pose tracking system. This system can be used for indoor tracking in VR and AR environments. Our approach uses video projectors to project a display grid pattern on the floor. The pattern consists of circular binary code color markers on a black and white checkerboard. A camera is attached to a user's back, looking down. The camera looks at the projected pattern, and its position is calculated in tracking space based on the correspondence between the global position of the display grid markers and their image coordinates. We calibrate for the offset between the user's head and the camera; hence head position can be calculated. The system has a mean position error of 4 millimeters and a mean jitter of less than 0.3 millimeters. We augment this position information with an inertial sensor to compute head rotation to achieve full 6DOF tracking.

Fast multiple camera head pose tracking

2003

Abstract This paper presents a multiple camera system to determine the head pose of people in an indoor setting. Our approach extends current eye tracking techniques from a single camera system to a multiple camera system. The head pose of a person is determined by triangulating multiple facial features that are obtained in real-time from eye trackers. Our work is unique in that it allows us to observe user head orientation in real-time using several cameras over a much larger space than covered by a single camera.

An efficient camera calibration method for vision-based head tracking

The aim of this study is to develop and evaluate an e$cient camera calibration method for vision-based head tracking. Tracking head movements is important in the design of an eye-controlled human/computer interface. A vision-based head tracking system is proposed to allow the user's head movements in the design of the eye-controlled human/computer interface. We propose an e$cient camera calibration method to track the three-dimensional position and orientation of the user's head accurately. We also evaluate the performance of the proposed method and the in#uence of the con"guration of calibration points on the performance. The experimental error analysis results showed that the proposed method can provide more accurate and stable pose (i.e. position and orientation) of the camera than the direct linear transformation method which has been used in camera calibration. The results for this study can be applied to the tracking of head movements related to the eye-controlled human/computer interface and the virtual reality technology.

Probabilistic Head Pose Tracking Evaluation in Single and Multiple Camera Setups

Lecture Notes in Computer Science, 2008

This paper presents our participation in the CLEAR 07 evaluation workshop head pose estimation tasks where two head pose estimation tasks were to be addressed. The first task estimates head poses with respect to (w.r.t.) a single camera capturing people seated in a meeting room scenario. The second task consisted of estimating the head pose of people moving in a room from four cameras w.r.t. a global room coordinate. To solve the first task, we used a probabilistic exemplar-based head pose tracking method using a mixed state particle filter based on a represention in a joint state space of head localization and pose variable. This state space representation allows the combined search for both the optimal head location and pose. To solve the second task, we first applied the same head tracking framework to estimate the head pose w.r.t each of the four camera. Then, using the camera calibration parameters, the head poses w.r.t. individual cameras were transformed into head poses w.r.t to the global room coordinates, and the measures obtained from the four cameras were fused using reliability measures based on skin detection. Good head pose tracking performances were obtained for both tasks.

Tridimensional pose estimation of a person head

Journal of Physics: Conference Series, 2007

In this work, we present a method for estimating 3-D motion parameters; this method provides an alternative way for 3D head pose estimation from image sequence in the current computer vision literature. This method is robust over extended sequences and large head motions and accurately extracts the orientation angles of head from a single view. Experimental results show that this tracking system works well for development a humancomputer interface for people that possess severe motor incapacity.

Multi-camera head pose estimation

Machine Vision and Applications, 2012

Estimating people's head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multiclass support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

Head pose estimation with one camera, in uncalibrated environments

International Workshop on Eye …, 2010

Head pose together with eye gaze are a reliable indication regarding the estimate of the focus of attention of a person standing in front of a camera, with applications ranging from driver's attention estimation to meeting environments. As gaze indication, eye gaze in non-intrusive or non highly specialized environments is, most times, difficult to detect and, when possible, combination with head pose is necessary. Also, in order to successfully track the rotation angles of the head, a priori knowledge regarding the equipment setup parameters is needed, or specialized hardware, that can be intrusive is required. Here, we propose a novel facial feature tracker that uses Distance Vector Fields (DVFs) and, combined with a new technique for face tracking, successfully detects facial feature positions during an image sequence and estimates head pose parameters. No a priori knowledge regarding camera or environmental parameters is needed for our technique.

The measurement of the angle of a user’s head in a novel head-tracker device

Measurement, 2006

The measurement of the angle of a user's head in a novel head tracker is presented. From the image of multi-lightsources the computer transforms the total positions into polar coordinates and determines the angle of the swinging head to improve the performance of mouse operation. Once the user has finished system adjustment and remained at the same distance, the user can precisely control the mouse to any position for any distance. This system can create the head's dynamic motional space using the relative positions of multi-light-sources. If the user is closer to the monitor, the user will achieve better system performance. This is great improvement on the inaccuracy of previous head-control systems and the angles of user's head swimming can control the speed of the cursor. This head tracker is more precise and has superior performance, especially for the icon clicking of a computer operation.

Fasthpe : a recipe for quick head pose estimation

2014

Estimating the head orientation of a person from a single camera is an important step for human-computer interaction, especially for widely available laptops and hand-held devices. This work aims to track a human face and estimate its orientation in the 6 degrees of freedom from an uncalibrated monocular camera, keeping the user free of any devices or wires. We propose a novel algorithm based on existing computer vision techniques for a real-time (2ms) head pose estimation system, which can start and recover from failure automatically without any previous knowledge of the user’s appearance or location. We demonstrate that this computationally ecient pose estimation system is able to track the continuous roll, yaw, and pitch angles within absolute errors of 3.03, 5.27 and 3.91 degrees respectively. We show that with the tracking of only four face features, it is possible to obtain continuous head orientation measurements in real-time (2ms).

Evaluation Of Marker Based Reference System For Head Pose Detection

The European Proceedings of Social and Behavioural Sciences, 2019

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