HUMAN ACTION RECOGNITION IN TEMPORAL-VECTOR TRAJECTORY LEARNING FRAMEWORK
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HUMAN ACTION RECOGNITION IN TEMPORAL-VECTOR TRAJECTORY LEARNING FRAMEWORK. 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. Outline. Motivation

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HUMAN ACTION RECOGNITION IN TEMPORAL-VECTOR TRAJECTORY LEARNING FRAMEWORK

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


Outline
Outline LEARNING FRAMEWORK

  • Motivation

  • System flowchart

  • Training Process

  • Testing Process

  • Experimental Results

  • Conclusions


Motivation
Motivation LEARNING FRAMEWORK

  • Traditional Manifold classification (ex: LDA , LSDA…)

    *Only spatial information

    *The input data are continuous sequences

    *Temporal information should be considered


System flowchart
System flowchart LEARNING FRAMEWORK

h*w

h*w

d

d

A

S

M

d*(2t+1)

d*(2t+1)

d*(2t+1)

d*(2t+1)


Training process
Training process LEARNING FRAMEWORK

LPP

Temporal data

Metric Learning


Dimension reduction
Dimension Reduction LEARNING FRAMEWORK

  • 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


Locality Preserving projection(1/2) LEARNING FRAMEWORK

Try to keep the local structure while reducing the dimension


Locality preserving projection 2 2
Locality Preserving projection(2/2) LEARNING FRAMEWORK

Objective function:

L : Laplacian matrix

Where L = (D - W)

D : Diagonal matrix

W : Weight matrix

Subject to


Temporal information
Temporal Information LEARNING FRAMEWORK

  • Three kinds of temporal information

    • LTM(Locations temporal motion of Mahalanobis distance)

    • DTM(Difference temporal motion of Mahalanobis distance)

    • TTM(Trajectory temporal motion of Mahalanobis distance)


LTM LEARNING FRAMEWORK

An input sequence:

LPP

Temporal

where


DTM LEARNING FRAMEWORK

where


TTM LEARNING FRAMEWORK

where


Metric learning
Metric Learning LEARNING FRAMEWORK

  • Mahalanobis distance

    • Preserving the relation of the data

    • Doesn’t depend on the scale of the data


LMNN LEARNING FRAMEWORK

Minimize :

yj

yi

yi

LMESpace

Subject to :

yl

yj

LPP+Temporal Space

(i)

yi

yi

yl

(ii)

LMNN

yj

yl

(iii) M has to be positive semi-definite


Recognition process
Recognition LEARNING FRAMEWORKprocess

LPP

Temporal data

Metric Learning

K-NN


KNN LEARNING FRAMEWORK

The number of nearest neighbor

Test data

3

1

Training data

1

The winner takes all~~

Labeled as

K=5


Experimental results 1 2
Experimental results(1/2) LEARNING FRAMEWORK


Experimental results 2 2
Experimental results(2/2) LEARNING FRAMEWORK


Conclusions
ConclusionS LEARNING FRAMEWORK

  • 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


Thank You!! LEARNING FRAMEWORK


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