568 research outputs found
Analysis of the hands in egocentric vision: A survey
Egocentric vision (a.k.a. first-person vision - FPV) applications have
thrived over the past few years, thanks to the availability of affordable
wearable cameras and large annotated datasets. The position of the wearable
camera (usually mounted on the head) allows recording exactly what the camera
wearers have in front of them, in particular hands and manipulated objects.
This intrinsic advantage enables the study of the hands from multiple
perspectives: localizing hands and their parts within the images; understanding
what actions and activities the hands are involved in; and developing
human-computer interfaces that rely on hand gestures. In this survey, we review
the literature that focuses on the hands using egocentric vision, categorizing
the existing approaches into: localization (where are the hands or parts of
them?); interpretation (what are the hands doing?); and application (e.g.,
systems that used egocentric hand cues for solving a specific problem).
Moreover, a list of the most prominent datasets with hand-based annotations is
provided
Benefits of temporal information for appearance-based gaze estimation
State-of-the-art appearance-based gaze estimation methods, usually based on
deep learning techniques, mainly rely on static features. However, temporal
trace of eye gaze contains useful information for estimating a given gaze
point. For example, approaches leveraging sequential eye gaze information when
applied to remote or low-resolution image scenarios with off-the-shelf cameras
are showing promising results. The magnitude of contribution from temporal gaze
trace is yet unclear for higher resolution/frame rate imaging systems, in which
more detailed information about an eye is captured. In this paper, we
investigate whether temporal sequences of eye images, captured using a
high-resolution, high-frame rate head-mounted virtual reality system, can be
leveraged to enhance the accuracy of an end-to-end appearance-based
deep-learning model for gaze estimation. Performance is compared against a
static-only version of the model. Results demonstrate statistically-significant
benefits of temporal information, particularly for the vertical component of
gaze.Comment: In ACM Symposium on Eye Tracking Research & Applications (ETRA), 202
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