1. 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 1

2. + Motivation + System flowchart + Training Process + Testing Process + Experimental Results + Conclusions 2

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

4. ASMASM h*w d d*(2t+1) h*w d d*(2t+1) 4

5. LPP Temporal data Metric Learning 5

6. + 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 6

7. Try to keep the local structure while reducing the dimension 7

8. Subject to Where L = (D - W) Objective function: L : Laplacian matrix D : Diagonal matrix W : Weight matrix 8

9. + Three kinds of temporal information 1.LTM(Locations temporal motion of Mahalanobis distance) 2.DTM(Difference temporal motion of Mahalanobis distance) 3.TTM(Trajectory temporal motion of Mahalanobis distance) 9

10. LTM An input sequence: LPP Temporal where 10

11. DTM where 11

12. TTM where 12

13. + Mahalanobis distance 1.Preserving the relation of the data 2.Doesn’t depend on the scale of the data 13

14. yiyi yiyi yjyj ylyl yjyj yiyi ylyl yiyi yjyj ylyl LME Space LMNN LPP+Temporal Space 14 Minimize : Subject to : (i) (ii) (iii ) M has to be positive semi-definite

15. LPP Metric Learning Temporal data K-NN 15

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

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19. + 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 19