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From Department of Electronics and
Communication Engineering
By M.K. Bhuyan, D. Ghosh and P.K. Bora
Appears in :Cybernetics and Intelligent Systems,
2006 IEEE Conference on
報告人:林福城
指導老師:陳定宏
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Outline
1.Introduction
2.Tracking Algorithm
A. Motion vector estimation
B. Trajectory estimation
C. Trajectory formation and smoothing of final trajectory
D. Key frame based trajectory estimation
3.Feature extraction from estimated trajectory
A. Static features
B. Dynamic features
C. Forming prototype feature vectors and knowledge-base for
gesture matching
4.Experimental results
5.Conclusions and discussion
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1.Introduction
最近，一個在影像辨識領域上受到大量注意的熱門
的研究主題是手勢辨識，手勢辨識為藉由不同的手的
形狀，位置，動作和移動，來表達某些訊息。
 在眾多研究裡，有研究者提出利用邊緣和剪影的特
性來追蹤手和手指。另外，也有研究者提出利用皮膚
的顏色來區分手的區域，但易受光線影響。
 選擇合適的特徵是手勢辨識中一個重要的關鍵，在
動態手勢辨識中，外形和位置是重要的，再來則是軌
跡。在本文，提出了利用軌跡點，軌跡長度，軌跡外
形，區域特徵，外部特徵來辨識手勢。

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2.1 Motion vector estimation
The hand pixels are assigned “1” and the background pixels are
assigned “0”. Thus, a binary model for the moving hand is
derived and is used for tracking. The Hausdorff object tracker
finds the position where the input hand model best matches the
next edge image and returns the motion vector MVi that
represents the best translation.
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2.2 Trajectory estimation
Determination of
centroid of hand image
I(x, y) is the pixel value at
the position (x, y) of the
image.
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2.3 Trajectory formation and
smoothing of final trajectory
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2.4 Key frame based trajectory
estimation
可以藉由選擇主要的視訊Frame，來減少預測手勢的軌
跡的計算量，選擇的方法是利用Hausdorff距離量測，轉
換一個全部視訊，到一個可代表特殊連續手勢的Frame的
短片，在取得主要Frame之後，藉由之前所提的程序，來
取得手的軌跡。
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3. Feature extraction form estimated
trajectory
 1.Static feature(low level feature)

Key trajectory point selection(選擇主要軌跡點)

Trajectory length calculation(計算軌跡長度)

Location feature extraction(位置特徵的取出)

Orientation feature extraction(方向特徵的取出)
 2.Daymic feature(High level feature)

Velocity feature(速度特徵)

Acceleration feature(加速度特徵)
 3.Forming prototype feature vectors and knowledge-
base for gesture matching
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Static feature-Key trajectory
point and Trajectory length
 1.選擇主要點的基本準則是比對預測軌跡的相鄰近似
值距離，直到內差值超過一臨界值。
 2.計算手的軌跡的總長度。
D   ( xi  xi 1 )  ( yi  yi 1 )
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Static featureLocation feature extraction
 The location feature is
the measure of the
distance between the
center of gravity and
the selected key points
in a gesture trajectory.
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Static featureOrientation feature extraction
Orientation feature gives the
direction along which the hand
traverses in space while making
a gesture.
1) Directions of hand movement at
the starting and ending points, i.e.,
θs = θ1 and θe = θN.
2) Number of points Nθ at which the
change in direction of hand movement
exceeds some predefined threshold Tθ, i.e.,
|θi+1 − θi| ≥ Tθ.
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Dynamic features- Velocity feature
 It is based on an important observation that each
gesture is made at different speeds.
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Dynamic featuresAcceleration feature(1)
 As mentioned earlier, continuous gestures are
composed of a series of gestures that as a whole
bears some meaning. As a first step towards
recognition, a continuous gesture sequence needs to
be segmented into its component gestures.
 In view of this, we propose acceleration feature
which may distinguish co-articulation phase from the
meaningful dynamic gesture sequence, as during
co-articulation hand moves very quickly.
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Dynamic featuresAcceleration feature(2)
 Computation of motion features
Average velocity over the whole trajectory length vavg.
Maximum and Minimum trajectory velocity vmax and Vmin.
Number of maxima (Nv,max) in the velocity profile.
Number of minima (Nv,min) in the velocity profile.
 Normalization of features
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Dynamic featuresForming prototype feature vectors and knowledge-base
for gesture matching
 Trajectory length l.
 Location feature Lavg.
 Starting hand orientation θs.
 Ending hand orientation θe.
 Number of significant changes in hand orientation Nθ.
 Average velocity vavg.
 Maximum velocity vmax.
 Minimum velocity vmin.
 Number of maxima in the velocity profile Nv,max.
 Number of minima in the velocity profile Nv,min.
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4. Experimental results(1)
 We have tested altogether ten different hand
trajectories in view of special applications like robot
control and gesture based window menu activation
in the Human-Computer interactive (HCI) platform.
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4. Experimental results(2)
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5.Conclusions and discussion
 The advantage of VOP based method for
segmentation of hand image is that no extra
computation for rotation and scaling of the object are
required.
 The proposed acceleration feature works nicely only
when the spatial end position of preceding gesture is
different from the start position of next gesture in the
connected gesture sequence.
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