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HUMAN ACTION RECOGNITION IN TEMPORAL-VECTOR TRAJECTORY LEARNING FRAMEWORK

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

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  1. 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

  2. Outline • Motivation • System flowchart • Training Process • Testing Process • Experimental Results • Conclusions

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

  4. System flowchart h*w h*w d d A S M d*(2t+1) d*(2t+1) d*(2t+1) d*(2t+1)

  5. Training process LPP Temporal data Metric Learning

  6. 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

  7. Locality Preserving projection(1/2) Try to keep the local structure while reducing the dimension

  8. Locality Preserving projection(2/2) Objective function: L : Laplacian matrix Where L = (D - W) D : Diagonal matrix W : Weight matrix Subject to

  9. 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)

  10. LTM An input sequence: LPP Temporal where

  11. DTM where

  12. TTM where

  13. Metric Learning • Mahalanobis distance • Preserving the relation of the data • Doesn’t depend on the scale of the data

  14. 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

  15. Recognition process LPP Temporal data Metric Learning K-NN

  16. KNN The number of nearest neighbor Test data 3 1 Training data 1 The winner takes all~~ Labeled as K=5

  17. Experimental results(1/2)

  18. Experimental results(2/2)

  19. 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

  20. Thank You!!

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