TY - GEN
T1 - Automatic hand detection in rgb-depth data sequences
AU - Konovalov, Vitaliy
AU - Clapés, Albert
AU - Escalera, Sergio
PY - 2013
Y1 - 2013
N2 - Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, view point variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point. In a given frame, the human body is segmented first in the depth image. Then, a graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths' connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures.
AB - Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, view point variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point. In a given frame, the human body is segmented first in the depth image. Then, a graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths' connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures.
KW - Geodesic paths
KW - Hand detection
KW - Human Pose Recovery
KW - Human-Computer Interaction
KW - Multi-modal RGB-Depth data
KW - Optical Flow
UR - http://www.scopus.com/inward/record.url?scp=84894803343&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-320-9-91
DO - 10.3233/978-1-61499-320-9-91
M3 - Article in proceeding
AN - SCOPUS:84894803343
SN - 9781614993193
T3 - Frontiers in Artificial Intelligence and Applications
SP - 91
EP - 100
BT - Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence
A2 - Gibert, Karina
A2 - Botti, Vicent
A2 - Reig-Bolano, Ramon
ER -