Title
Real-time both hands tracking using CAMshift with motion mask and probability reduction by motion prediction
Abstract
Hand gesture interfaces are more intuitive and convenient than traditional interfaces. They are the most important parts in the relationship between users and devices. Hand tracking for hand gesture interfaces is an active area of research in image processing. However, previous works have limits such as requiring the use of multiple camera or sensor, working only with single color background, etc. This paper proposes a real-time both hands tracking algorithm based on “CAMshift (Continuous Adaptive Mean Shift Algorithm)” using only a single camera in multi-color backgrounds. In order to track hands robustly, the proposed algorithm uses “motion mask” to combine color and movement probability distributions and “probability reduction” for multi-hand tracking in non-limiting environments. Experimental results demonstrate that this algorithm can precisely track both hands of an operator in multi-color backgrounds and process the VGA size input sequences from a web camera in real time (about 25 fps).
Year
Venue
Keywords
2012
Signal & Information Processing Association Annual Summit and Conference
cameras,gesture recognition,image colour analysis,image motion analysis,image sequences,object tracking,statistical distributions,CAMshift,VGA size input sequence,Web camera,continuous adaptive mean shift algorithm,hand gesture interface,image processing,motion mask,motion prediction,multicolor background,multihand tracking,nonlimiting environment,probability distribution,probability reduction,real-time both hands tracking algorithm
Field
DocType
ISSN
Computer vision,Gesture,Computer science,Gesture recognition,Image processing,Video tracking,Probability distribution,Operator (computer programming),Artificial intelligence,Mean-shift,Video Graphics Array
Conference
2309-9402
ISBN
Citations 
PageRank 
978-1-4673-4863-8
2
0.39
References 
Authors
6
3
Name
Order
Citations
PageRank
Ryosuke Araki120.39
Seiichi Gohshi27118.16
Takeshi Ikenaga3618125.50