This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their posterior probability density function. Marginalizing variables reduces the dimension of the configuration space and makes the particle filter mor...

Mixture models have been popular to solve computer vision problems such as optical flow, object recognition and human pose estimation. In particular, mixtures of classifiers are state-of-the-art approaches to estimate human pose from images. These discriminative approaches learn a functional mapping, or conditional distributions, between image features and 3D poses. However, existing algorithms...

The topic of this article is a basic research considering a humancomputer interaction. The system is still under construction, however its basis – facial features tracking and head pose estimation – is ready to use, thus it could bring a head gesture controlled system into reality. We present an approach to control applications with head movements. We construct an Active Appearance Model (AAM) ...

This paper presents the detection of 3D pose of moving humans heads. The contribution of this work is the conjunction of an e ective preprocessing method and a strong estimation Kalman technique which uses less noise sensitive 3D line observations. The procedure involves the detection of the target face in any type of background using color histograms. To outline the face we use a minimization ...

We present a method for estimating the point of fixation of an air traffic controller from a low resolution video sequence. A geometric model of the head is used to estimate head orientation; head pose estimates are combined with a 3D model of the environment to compute the target of gaze. The head model is constructed from a small set of images. Two methods are considered: in the first, we tre...

Head pose estimation is a crucial step for numerous face applications such as gaze tracking and face recognition. In this paper, we introduce a new method to learn the mapping between a set of features and the corresponding head pose. It combines a filter based feature selection and a Generalized Regression Neural Network where inputs are sequentially selected through a boosting process. We pro...

In this paper we approach the problem of head pose estimation by combining Multi-scale Gaussian Derivatives with Support Vector Machines. We evaluate the approach on the Pointing04 and CMU-PIE data sets and to estimate the pan and tilt of the head from facial images. We achieved a mean absolute error of 6.9 degrees for pan and 8.0 degrees for tilt on the Pointing04 data set.

This paper addresses the problem of head pose estimation in order to infer non-intrusive feedback fromusers about gaze attention. The proposed approach exploits the bilateral symmetry of the face. Size and orientation of the symmetrical area of the face is used to estimate roll and yaw poses by themean of decision tree model. The approach does not need the location of interest points on face an...

This paper presents regression methods for estimation of head pose from occluded 2-D face images. The process primarily involves reconstructing a face from its occluded image, followed by classification. Typical methods for reconstruction assume that the pixel errors of the occluded regions are independent. However, such an assumption is not true in the case of occlusion, because of its inheren...

Existing works on 2D pose estimation mainly focus a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting the poses/keypoints unseen class objects. In this paper, we introduce task Category-Agnostic Pose Estimation (CAPE), which aims to create model capable any object given only few samples with keypoint definition. To achieve...