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Cylindrical model based head pose estimation for drivers
Abstract
The application of action recognition algorithms onto driving safety systems is still an open area of research. In terms of driving safety, identification of head movements present more significant information in comparison to other actions of the driver. Therefore, in this study, we developed a cylindrical model based head pose estimator to track drivers' head movements. The experiments indicate that the proposed scheme presents significant accuracy in estimation of head pose.
Estimation of human head position and orientation has become an increasingly important issue in human-computer interaction field. Over the last decade, many approaches have been introduced to achieve head pose estimation in both academical and industrial fields, but the low-cost and real-time application still proves to be a difficult task. Motivated by the past researches, we propose an automatic and monocular head pose estimation system. We applied a number of improvements to a direct linear transformation algorithm called Pose from Orthography and Scaling with ITerations (POSIT) and applied it for the head pose estimation. User's virtual head model is also recovered by analyzing laser scan database and corrected by the head pose. The entire process is completely automatic with no need for users to pre-register for identification or initialize their head position. Experiments on a public dataset show realtime performance with lower errors than previous head pose estimation methods.
Head pose and eye location estimation are two closely related issues which refer to similar application areas. In recent years, these problems have been studied individually in numerous works in the literature. Previous research shows that cylindrical head models and isophote based schemes provide satisfactory precision in head pose and eye location estimation, respectively. However, the eye locator is not adequate to accurately locate eye in the presence of extreme head poses. Therefore, head pose cues may be suited to enhance the accuracy of eye localization in the presence of severe head poses. In this paper, a hybrid scheme is proposed in which the transformation matrix obtained from the head pose is used to normalize the eye regions and, in turn the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) to extend the operating range of the eye locator and (3) to improve the accuracy and re-initialization capabilities of the pose tracker. From the experimental results it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Further, it considerably extends its operating range by more than 15deg, by overcoming the problems introduced by extreme head poses. Finally, the accuracy of the head pose tracker is improved by 12% to 24%.
The ubiquitous application of eye tracking is precluded by the requirement of dedicated and expensive hardware, such as infrared high definition cameras. Therefore, systems based solely on appearance (i.e. not involving active infrared illumination) are being proposed in literature. However, although these systems are able to successfully locate eyes, their accuracy is significantly lower than commercial eye tracking devices. Our aim is to perform very accurate eye center location and tracking, using a simple Web cam. By means of a novel relevance mechanism, the proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve rotational invariance and to keep low computational costs. In this paper we test our approach for accurate eye location and robustness to changes in illumination and pose, using the BioIDand the Yale Face B databases, respectively. We demonstrate that our system can achieve a considerable improvement in accuracy over state of the art techniques.
Head tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. However, precision in head pose estimation is dependent on tracking accuracy which itself could benefit from the head orientation knowledge. Therefore, this work considers head tracking and pose estimation as two coupled problems in a probabilistic setting. Head pose models are learned and incorporated into a mixed-state particle filter framework for joint head tracking and pose estimation. Experimental results on real sequences show the effectiveness of the method in estimating more stable and accurate pose values.
A robust pose estimation approach is proposed by combining facial appearance asymmetry and 3D geometry in a coarse-to-fine framework. The rough face pose is first estimated by analyzing the asymmetry of the distribution of the facial component detection confidences on an image, which actually implies an intrinsic relation between the face pose and the facial appearance. Then, this rough face pose, as well as error bandwidth, is utilized into a 3D-to-2D geometrical model matching to refine the pose estimation. The proposed approach is able to track a face with fast motion in front of cluttered background and recover its pose robustly and accurately in real- time. Experiment results are provided to demonstrate its efficiency and accuracy.
Driver distraction is an important issue in developing new generation of telematic systems. Our research is focused on development of novel machine vision systems, which can provide better understanding of the state of the driver and driving conditions. In this paper we discuss in detail on the development of a system which allows simultaneous capture of the driver's head pose and driving view. The system utilizes a full 360 degree panoramic field of view using a single video stream. The integrated machine vision system includes modules of perspective transformation, feature extraction, head detection, head pose estimation, and driving view synthesis. The paper presents a multi-state statistical decision models with Kalman filtering based tracking for head pose detection and face orientation estimation. The basic feasibility and robustness of the approach is demonstrated with the help of a series of systematic experimental studies.
Results of the 1987-1988 anthropometric survey of Army personnel are presented in this report in the form of summary statistics, percentile data and frequency distribution. These anthropometric data are presented for a subset of personnel (1774 men and 2208 women) sampled to match the proportions of age categories and racial/ethnic groups found in the active duty Army of June 1988. Dimensions given in this report include 132 standard measurements made in the course of the survey, 60 derived dimensions calculated largely by adding and subtracting standard measurement data, and 48 head and face dimensions reported in traditional linear terms but collected by means of an automated headboard designed to obtain three-dimensional data. Measurement descriptions. Descriptions of the procedures and techniques used in this survey are also provided. These include explanations of the complex sampling plan, computer editing procedures, and strategies for minimizing observer error. Tabular material in appendices are designed to help users understand various practical applications of the dimensional data, and to identify comparable data obtained in previous anthropometric surveys.
The active appearance models (AAMs) provide the detailed descriptive parameters that are useful for various autonomous face
analysis problems. However, they are not suitable for robust face tracking across large pose variation for the following reasons.
First, they are suitable for tracking the local movements of facial features within a limited pose variation. Second, they
use gradient-based optimization techniques for model fitting and the fitting performance is thus very sensitive to initial
model parameters. Third, when their fitting is failed, it is difficult to obtain appropriate model parameters to re-initialize
them. To alleviate these problems, we propose to combine the active appearance models and the cylinder head models (CHMs),
where the global head motion parameters obtained from the CHMs are used as the cues of the AAM parameters for a good fitting
or re-initialization. The good AAM parameters for robust face tracking are computed in the following manner. First, we estimate
the global motion parameters by the CHM fitting algorithm. Second, we project the previously fitted 2D shape points onto the
3D cylinder surface inversely. Third, we transform the inversely projected shape points by the estimated global motion parameters.
Fourth, we project the transformed 3D points onto the input image and computed the AAM parameters from them. Finally, we treat
the computed AAM parameters as the initial parameters for the fitting. Experimental results showed that face tracking combining
AAMs and CHMs is more pose robust than that of AAMs in terms of 170% higher tracking rate and the 115% wider pose coverage.
Driver distraction and inattention are prominent causes of automotive collisions. To enable driver assistance systems to address these problems, we require new sensing approaches to infer a driverpsilas focus of attention. In this paper, we present a new 3D tracking algorithm and integrate it into HyHOPE, a real-time (30 fps) hybrid head orientation and position estimation system for driver head tracking. With a single video camera, the system continuously tracks the head in six degrees-of-freedom, initializing itself automatically with separate modules for head detection and head pose estimation. The tracking module provides a fine estimate of the 3D motion of the head, using a new appearance-based algorithm for 3D model tracking by particle filtering in an augmented reality environment. We describe our implementation, which utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real-time. To quantitatively evaluate the accuracy of our system, we compare its estimation results to a marker-based cinematic motion capture system installed in an automotive testbed. We evaluate the system on real daytime and nighttime drives with drivers of varying ages, race, and sex.
This paper describes a real-time prototype computer vision system for monitoring driver vigilance. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver's level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was tested in a simulating environment with subjects of different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate.
To build smart human interfaces, it is necessary for a system to
know a user's intention and point of attention. Since the motion of a
person's head pose and gaze direction are deeply related with his/her
intention and attention, detection of such information can be utilized
to build natural and intuitive interfaces. We describe our real-time
stereo face tracking and gaze detection system to measure head pose and
gaze direction simultaneously. The key aspect of our system is the use
of real-time stereo vision together with a simple algorithm which is
suitable for real-time processing. Since the 3D coordinates of the
features on a face can be directly measured in our system, we can
significantly simplify the algorithm for 3D model fitting to obtain the
full 3D pose of the head compared with conventional systems that use
monocular camera. Consequently we achieved a non-contact, passive,
real-time, robust, accurate and compact measurement system for head pose
and gaze direction
Computer systems which analyse human face/head motion have attracted significant attention recently as there are a number of interesting and useful applications. Not least among these is the goal of tracking the head in real time. A useful extension of this problem is to estimate the subject's gaze point in addition to his/her head pose. This paper describes a real-time stereo vision system which determines the head pose and gaze direction of a human subject. Its accuracy makes it useful for a number of applications including human/computer interaction, consumer research and ergonomic assessment
Head pose estimation is important for driver attention monitoring as well as for various human computer interaction tasks. In this paper, an adaptive head pose estimation method is proposed to overcome difficulties of existing approaches. The proposed method is based on the analysis of two approaches for head pose estimation from an image sequence, that is, principal component analysis (PCA) and 3D motion estimation. The algorithm performs accurate pose estimation by learning the subject appearance on-line. Depth information is used effectively in the algorithm to segment the head region even in a cluttered scene and to perform 3D head motion estimation based on optical flow constraints.
The goal of this work is to classify the focus of attention of a
subject who is switching his or her attention between a number of
surrounding objects. The specific application is to classify, the focus
of attention of a car driver as straightahead, towards the dashboard, or
towards one of the mirrors. A quantitative approach to this problem
requires (a) a priori information about the interior geometry of the car
and the calibration of the camera, and (b) accurate computation of the
subject's location and eye direction. This paper describes an
alternative, qualitative, approach. The driver is observed over an
extended period of time, and frequently occurring view directions are
identified . Each of the frequent view directions is associated with its
most likely target-dashboard, rear-view mirror etc. The head pose of the
driver in any subsequent image is then classified by relating it to one
of these labelled view directions. The head pose classification is
augmented with a qualitative measurement of the eye pose
Anthropo-metric Survey of US Army Personnel: Methods and Sum-mary Statistics
- C C Gordon
- T Churchill
- Ce Clauser
- B Bradtmiller
- J T Mcconville
- I Tebbetts
- R A Walker
C.C. Gordon, T. Churchill, CE Clauser, B. Bradtmiller, J.T. McConville, I. Tebbetts, and R.A. Walker. Anthropo-metric Survey of US Army Personnel: Methods and Sum-mary Statistics. US Army Natick Research Development and Engineering Center Natick Massachusetts Tech. Re-port, 1989. 978-1-4244-4436-6/09/$25.00 ©2009 IEEE
from camera head pose to 3d global room head pose using multiple camera views
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Robustfying Eye Center Localization Using Head Pose Cues. Computer Vision and Pattern Recognition
- R Valenti
- Z Yücel
- T Gevers
R. Valenti, Z. Yücel, and T. Gevers. Robustfying Eye Center Localization Using Head Pose Cues. Computer Vision
and Pattern Recognition, IEEE Conf. on, 2009.