13,258 research outputs found
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions
A smart vehicle should be able to understand human behavior and predict their
actions to avoid hazardous situations. Specific traits in human behavior can be
automatically predicted, which can help the vehicle make decisions, increasing
safety. One of the most important aspects pertaining to the driving task is the
driver's visual attention. Predicting the driver's visual attention can help a
vehicle understand the awareness state of the driver, providing important
contextual information. While estimating the exact gaze direction is difficult
in the car environment, a coarse estimation of the visual attention can be
obtained by tracking the position and orientation of the head. Since the
relation between head pose and gaze direction is not one-to-one, this paper
proposes a formulation based on probabilistic models to create salient regions
describing the visual attention of the driver. The area of the predicted region
is small when the model has high confidence on the prediction, which is
directly learned from the data. We use Gaussian process regression (GPR) to
implement the framework, comparing the performance with different regression
formulations such as linear regression and neural network based methods. We
evaluate these frameworks by studying the tradeoff between spatial resolution
and accuracy of the probability map using naturalistic recordings collected
with the UTDrive platform. We observe that the GPR method produces the best
result creating accurate predictions with localized salient regions. For
example, the 95% confidence region is defined by an area that covers 3.77%
region of a sphere surrounding the driver.Comment: 13 Pages, 12 figures, 2 table
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.Comment: 15 pages, 8 figures, 6 table
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