Authors:
Stijn De Beugher
;
Geert Brône
and
Toon Goedemé
Affiliation:
KU Leuven, Belgium
Keyword(s):
Eye-tracking, Hand detection, Hand tracking, Human-human interaction, Gaze, (Semi-)automatic analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Mobile Imaging
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
In this paper we present a highly accurate algorithm for the detection of human hands in real-life 2D image sequences. Current state of the art algorithms show relatively poor detection accuracy results on unconstrained, challenging images. To overcome this, we introduce a detection scheme in which we combine several well known detection techniques combined with an advanced elimination mechanism to reduce false detections. Furthermore we present a novel (semi-)automatic framework achieving detection rates up to 100%, with only minimal manual input. This is a useful tool in supervised applications where an error-free detection result is required at the cost of a limited amount of manual effort. As an application, this paper focuses on the analysis of video data of human-human interaction, collected with the scene camera of mobile eye-tracking glasses. This type of data is typically annotated manually for relevant features (e.g. visual fixations on gestures), which is a time-consuming,
tedious and error-prone task. The usage of our semi-automatic approach reduces the amount of manual analysis dramatically. We also present a new fully annotated benchmark dataset on this application which we made publicly available.
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