Reliable hand detection and tracking in passive 2D video still remains a challenge. Yet the consumer market for gesture-based interaction is expanding rapidly and surveillance systems that can deduce fine-grained human activities involving hand and arm postures are in high demand. In this paper, we present a hand tracking method that does not require reliable detection. We built it on top of "Flocks of Features" which combines grey-level optical flow, a "flocking" constraint, and a learned foreground color distribution. By adding probabilistic (instead of binary classified) detections based on grey-level appearance as an additional image cue, we show improved tracking performance despite rapid hand movements and posture changes. This helps overcome tracking difficulties in texture-rich and skin-colored environments, improving performance on a 10-minute collection of video clips from 75% to 86% (see examples on our website). © 2010 Springer-Verlag.
CITATION STYLE
Kölsch, M. (2010). An appearance-based prior for hand tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6475 LNCS, pp. 292–303). https://doi.org/10.1007/978-3-642-17691-3_27
Mendeley helps you to discover research relevant for your work.