1. View-Invariant Representation and Recognition of actions
Paper By: Rao,Yilmaz and Shah)
International Joural of Computer Vision
50(2), ,2002
Presented By:Xiangdong Wen
Advisor: L.J. Latecki

2. Introduction
Related work
Perception of Motion
Representation
Learning
Experiments
Conclusion

3. Natural Actions Events: 1.Low level description:
change of direction,stop, pause,
2.High level description:
opening a door,starting a car
Temporal textures:
ripples on the water,
a cloth waving in the wind
Activities:
walking, running, jumping

4. Recognition of human actions
Extract relevant information.
Represent it in a suitable form.
an abstraction of the data
view-invariant,compact,reliable
Interpret visual information.
recognition
learning

5. Related work Izumi and Kojiama(2000) head & model
Siskind and Moris(1996) HMM system
Davis et al.(2000) a sinusoidal model
Polana(1994) normal flow
Madabushi and Aggarwal(2000) head
Seitz and Dyer(1997) cyclic motion
Tsai et al.(1994) FFT find the period
Bobick and Davis(1997) aerobic exercise

6. Perception of Motion Human Perception Spatio-Temporal Curvature
How it Captures motion boundaries
Previous Approaches
Generate and Smooth of Trajectories

7. Human Perception
Dynamic instant: an instantaneous entity that occurs for only one frame.
Interval:the time period between two dynamic instants.

8. Sample movies(1)

9. Sample Movies(2)

10. Spatio-Temporal Curvature
r=[x(t),y(t),t]
v=[x’(t),y’(t),1]
a=[x’’(t),y’’(t),0]
||r’(t) X r’’(t)||
K(t)=
||r’(t)||^3

11. Previous Approaches Rubin and Richards(1985)
Polar coordinates, they using s’’(t) and Angle’’(t) separately.
Gould and Shah(1989)
Velocity vector v(t)=[v_x,v_y]
Trajectory Primal Sketch(TPS)
Both have alignment problem.

12. Generating and Smoothing of Trajectories
Skin detection by Kjeldean and Kender(1996).
Mean-shift tracking by Comaniciu et al.(2000).
Anisotropic diffusion smoothing by Perona and malik(1990).

13. Representation Instants Time:the frame number
Position: the position of the hand
Sign: of the turning angle
intervals

14. View-Invariance
The number of instants
The signs of instants

15. Learning Starting with no modal Matching two actions
1.same number of instants with same sign.
2.Use hand positions to form a 4*n matrix M, If Rank(M)<4 then ‘match’
Match error =|_4|.

16. Experiments
Using 47 different action clips performed by 7 individuals.
At most one hand in each frame.
Compare the speed to find the hand.
Results are amazing.

17. Conclusion View-invariant Dynamic instants and intervals
Spatio-temporal curvature
The system Learns without training
Experiments results are good

18. THANK YOU!