Chin-Hsien Fang( 方競賢 ), Ju-Chin Chen( 陳洳瑾 ), Chien-Chung Tseng( 曾建中 ),and...

Chin-Hsien Fang(方競賢 ), Ju-Chin Chen(陳洳瑾 ), Chien-Chung Tseng(曾建中 ),and Jenn-Jier James Lien(連震杰 )

Department of Computer Science and Information Engineering,National Cheng Kung University

HUMAN ACTION RECOGNITION IN TEMPORAL-VECTOR TRAJECTORY LEARNING FRAMEWORK

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+ Motivation+ System flowchart+ Training Process+ Testing Process+ Experimental Results + Conclusions

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+ Traditional Manifold classification (ex: LDA , LSDA…) *Only spatial information *The input data are continuous sequences *Temporal information should be considered

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ASM

h*w

d

d*(2t+1)

d*(2t+1)

h*w

d

d*(2t+1)

d*(2t+1)

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LPP

Temporal data

Metric Learning

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+ Why dimension reduction?– To reduce the calculation cost

+ Why LPP (Locality Preserving Projections)?– Can handle non-linear data with linear transformation matrix

– Local structure is preserved

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Try to keep the local structure while reducing the dimension

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ijij

jT

iT Wxaxa

2)(minarg

ij

TTijj

Ti

T aXLXaWxaxa 2)(2

1

1aXDXa TTSubject to

Where L = (D - W)

Objective function:

L : Laplacian matrixD : Diagonal matrixW : Weight matrix

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+ Three kinds of temporal information1. LTM(Locations temporal motion of Mahalanobis

distance)2. DTM(Difference temporal motion of Mahalanobis

distance)3. TTM(Trajectory temporal motion of Mahalanobis

distance)

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LTM

An input sequence: },......,{ 21 nxxxX LPP

},......,{ 21 nyyyY

Temporal

}',......','{' 21 nyyyY

],...,,,,...,[' 11 tiiiitii yyyyyy where

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DTM

}',......','{' 21 nyyyY

],...,,,,...,[' 11 tiiiiiiitiii yyyyyyyyyy

where

1iy

1iy

iy1 ii yy1 ii yy

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TTM

}',......','{' 21 nyyyY

],...,,,,...,[' 1111 titiiiiiititii yyyyyyyyyy

where

1iy 1iy

iy

ii yy 1

1 ii yy

2iy21 ii yy

12 ii yy

2iy 12

+ Mahalanobis distance1. Preserving the relation of the data

2. Doesn’t depend on the scale of the data

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yiyi

yj

yl

yj

yi

yl

yi

yj

yl

LME Space

LMNN

LPP+Temporal Space

)12*( tdiy

ii yy E

iy

)12*( tdiy

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ijk

ijkikijij

jiT

jiij YYMYY )1()()( ''''

Minimize :

Subject to :

ijkjiT

jikiT

ki YYMYYYYMYY 1)()()()( ''''''''

0ijk

(i)

(ii)

(iii) M has to be positive semi-definite

LPP

Metric Learning

Temporal data

K-NN

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Test data

Training data

3

1

1

K=5

The winner takes all~~

Labeled as

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The number of nearest neighbor

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+ Our TVTL framework makes impressive progress compared to other traditional methods such as LSDA

+ Temporal information do have positive influence

+ DTM , TTM are better than LTM because they consider the correlation of the data

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