127 research outputs found
On the Use of Speech and Face Information for Identity Verification
{T}his report first provides a review of important concepts in the field of information fusion, followed by a review of important milestones in audio-visual person identification and verification. {S}everal recent adaptive and non-adaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the non-adaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions. {T}his report is an extended and revised version of {IDIAP-RR} 02-33
Information Fusion and Person Verification Using Speech & Face Information
This report provides an overview of important concepts in the field of information fusion, followed by a review of literature pertaining to audio-visual person identification & verification. Several recent adaptive and non-adaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on audio and visual information, are evaluated in clean and noisy conditions on a common database using a text-independent setup. It is shown that in clean conditions all the non-adaptive approaches provide similar performance; in noisy conditions they exhibit deterioration in their performance. It is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision surface is fixed but constructed to take into account the effects of noisy conditions, providing a good trade-off between performance in clean and noisy conditions
Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos
Hyperspectral object tracking has recently emerged as a topic of great
interest in the remote sensing community. The hyperspectral image, with its
many bands, provides a rich source of material information of an object that
can be effectively used for object tracking. While most hyperspectral trackers
are based on detection-based techniques, no one has yet attempted to employ
YOLO for detecting and tracking the object. This is due to the presence of
multiple spectral bands, the scarcity of annotated hyperspectral videos, and
YOLO's performance limitation in managing occlusions, and distinguishing object
in cluttered backgrounds. Therefore, in this paper, we propose a novel
framework called Hy-Tracker, which aims to bridge the gap between hyperspectral
data and state-of-the-art object detection methods to leverage the strengths of
YOLOv7 for object tracking in hyperspectral videos. Hy-Tracker not only
introduces YOLOv7 but also innovatively incorporates a refined tracking module
on top of YOLOv7. The tracker refines the initial detections produced by
YOLOv7, leading to improved object-tracking performance. Furthermore, we
incorporate Kalman-Filter into the tracker, which addresses the challenges
posed by scale variation and occlusion. The experimental results on
hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in
accurately tracking objects across frames
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