Hand Pose Tracking and Hand Model Reconstruction from a Single Depth Camera
Date Issued
2015
Date
2015
Author(s)
Liu, Shih-Ming
Abstract
Hand pose tracking is gradually popular with diverse applications including gesture or sign language understanding, augmented reality and especially in human-computer interaction. Therefore, it is also a competitive system as a next natural step for communication in desktop or mobile environments. Moreover, depth sensors are more available in market and information from depth sensors is more helpful than normal RGB images to capture 3D positions of objects. Thus, the applications of commodity depth camera become useful and attractive. Nonetheless, this problem is challenging due to highly articulated hand poses, noisy input and severe self-occlusions. We propose a model-based hand pose tracking approach using a single depth camera. The method tracks a full hand and reconstructs a hand model with an accuracy pose to fit the current frame. The tracking system can be regarded as an optimization problem and we obtain the optimal hand pose with depth, silhouette, collision and temporal information. In addition, we improve the stochastic optimization method with an additional factor based on gradient descent for articulated hand tracking. In order to solve the complicated articulation problem, we present a novel strategy combining with an evolution algorithm which is used to reduce the high-dimension pose space.
Subjects
hand pose tracking
model reconstruction
stochastic optimization
depth camera
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02944013-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):7b883bb12e6accdf639534e58c7a6f21