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Real Time Appearance Based Hand Tracking
The 19th International Conference on Pattern Recognition (ICPR)December 7-11, 2008, Tampa Convention Center, Tampa, FL, USA
報告者:彭成瑋日期: 2009/12/29指導教授:陳立祥 教授實驗室:網際網路多媒體應用實驗室
Introduction
Hand tracking is an important problem in the field of human-computer interaction.
Application :sign language recognition or controlling computer games.
Model-based(3D model) and Appearance-based (Image features)
Introduction ( Cont. ) the hand presents a motion of 27 degrees of fre
edom (DOF), 21 for the joint angles and 6 for orientation and location[11, 10].
Substantial problems :out-of-plane rotations scale changes, self-occlusions or segmentation accuracy.
Real-time tracking performance Maximally Stable Extremal
Region (MSER) tracking algorithm.
Color likelihood
calculate a probability value p(O|xi) for every pixel in the current frame
object-to-be-tracked (hand) O Kullback-Leibler distance instead of the
Bhattacharyya distance The integral image for Bhattacharyya dist
ance calculation
Color likelihood ( Cont. ) Mahalanobis Distance
Bhattacharyya Distance
222
111
;
;
N
N
21
1
212121 2
,
TMAd
2
1
22
1
1
21
21
1
212121
2
1
ln2
1
28
1,
TBAd
Color likelihood ( Cont. ) color likelihood value -- p(O|xi) every pixel – xi r × c window color distribution of the hand O in the frame t−1 -- G
aussian 3×1 mean vector – μO 3×3 covariance matrix -- Gaussian multivariate Gaussian --
OOON ,
BBBN
iwN
B
iwO
iw
Oiw
NNNN
NNxiOp
,,
,exp|
Maximally Stable Extremal Region (MSER) tracking
(a) Input Image (b) Image histogram (c) MSER result
Experiments ( Cont. ) A simple gesture recognition allows to us
e the tracker for controlling the mouse pointer and activating mouse-clicks.
Conclusion
Novel real time method for tracking hands through image sequences
Efficiently calculated color similarity maps