Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is ...

Recent advances in wearable sensing allow active control of the orientation of a body-mounted camera worn by a remote user. In this paper we consider the control of the active camera from head movements. In the context of teleoperation, these may be the head movements of a remote operator, perhaps acting as the wearer’s assistant. The movements are likely to be larger than those in video-confer...

In this paper we propose a method for evaluating the dynamic blind spot of an operator of a construction vehicle by integrating static blind spot map of a construction vehicle with the head orientation of the operator. By computing the position and orientation of the equipment operator’s head, the field-of-view (FOV) of the operator is known which is projected on the blind spot map of the vehic...

We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range ima...

SYNOPSIS: The Vhuman-Embodiment project is a training simulation developed at the Institute of Creative Technologies in Los Angeles. In order to make the simulation more realistic, the user's head pose is used to provide the simulation with information on the user's attention. The purpose of this thesis has been to develop a system capable of estimating the head pose of the user. The system mus...

Head pose estimation is a fundamental problem in computer vision. Several methods has been proposed to solve this problem. Most existing methods use traditional computer vision methods and existing method of using neural networks works on depth bitmaps. In this project, we explore using convolutional neural networks (CNNs) that take RGB image as input to estimate the head pose. We use regressio...

We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, and roll) and translations perpendicular to the camera principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera’s principal ax...