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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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slide1

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
  • Motivation
  • System flowchart
  • Training Process
  • Testing Process
  • Experimental Results
  • Conclusions
motivation
Motivation
  • Traditional Manifold classification (ex: LDA , LSDA…)

*Only spatial information

*The input data are continuous sequences

*Temporal information should be considered

system flowchart
System flowchart

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

LPP

Temporal data

Metric Learning

dimension reduction
Dimension Reduction
  • 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
slide7

Locality Preserving projection(1/2)

Try to keep the local structure while reducing the dimension

locality preserving projection 2 2
Locality Preserving projection(2/2)

Objective function:

L : Laplacian matrix

Where L = (D - W)

D : Diagonal matrix

W : Weight matrix

Subject to

temporal information
Temporal Information
  • 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)
slide10

LTM

An input sequence:

LPP

Temporal

where

slide11

DTM

where

slide12

TTM

where

metric learning
Metric Learning
  • Mahalanobis distance
    • Preserving the relation of the data
    • Doesn’t depend on the scale of the data
slide14
LMNN

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 process

LPP

Temporal data

Metric Learning

K-NN

slide16
KNN

The number of nearest neighbor

Test data

3

1

Training data

1

The winner takes all~~

Labeled as

K=5

conclusions
ConclusionS
  • 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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