Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm

1. Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm byJames Dennis Musick

2. Agenda • Introduction • Problem Definition • Centroid Algorithm • Kalman Filter • Target Discrimination • Conclusion • Future Work

3. Introduction • In the field of biomechanical research there is a subcategory that studies human movement or activity by video-based analysis • Markers used • Optical • RF • Passive reflective • Etc… • Video based motion analysis • 2D Analysis • 3D analysis • Golf swing example

4. Problem Definition • In order to track the following have to be accomplished • Centroid calculation • Prediction • Discrimination

5. Problem Definition cont. • Trials used • Walking Trial • Jumping Trial • Waving Wand Trial • Increasing complexity

6. Centroid Algorithm • Introduction • Scanning scheme

7. Threshold X/Y address location Target Discrimination Buffer Logic control and centroid calculation Centroid Value Memory Centroid Algorithm cont. • 640 x 480 • ~ 307200 pixels • 8-bit Gray-scale • Block diagram

8. Centroid Algorithm cont. • Threshold

9. Centroid Algorithm cont. • x/y addressing

10. Centroid Algorithm cont. • Target Pixel Discrimination Buffer • x_sum, y_sum, LS_target, RS_target, Bot_target, target_pixel_num

11. Centroid Algorithm cont. • Logic Control and Centroid Calculation

12. Centroid Algorithm cont. • Centroid Memory Buffer • Once a target is completed (defined as no pixels within the search criteria at the row just below the target), then the centroid data is stored in a memory array until the data is read out at the end of the number of pictures that are being analyzed. • The array would be structured in the following manner if there were three targets in each of 5 pictures: • Target_Centroid_Array = (xy,Target #, Picture #) => (1:2, 1:3, 1:5).

13. Centroid Algorithm cont. • Examples

14. Centroid Algorithm cont. • Performance and Limitations • Three targets simultaneous • Total number

15. Centroid Algorithm cont. • Measurement Uncertainty • Correct (3.5,4) Correct (3.5,3) • Blue missing (3.5,4) Red missing (3.8,3.17) • Red missing (3.64, 4.21)

16. Kalman Filter • Introduction • State Space representation

17. Kalman Filter cont.

18. Kalman Filter cont

19. Kalman Filter cont

20. Kalman Filter cont • Target Models: • Noisy Acceleration model

21. Kalman Filter cont • Target Models: • Noisy Jerk model

22. Kalman Filter cont • Selection of update time: • T = 1

23. Kalman Filter cont • b

24. Kalman Filter cont • Operation of the Kalman Filter

25. Kalman Filter cont • Operation of the Kalman Filter

26. Kalman Filter cont • Operation of the Kalman Filter

27. Kalman Filter cont • Operation of the Kalman Filter

28. Kalman Filter cont • Operation of the Kalman Filter

29. Kalman Filter cont • Operation of the Kalman Filter

30. Target Discrimination • Introduction • Goal

31. Target Discrimination • Example

32. Target Discrimination • Example cont

33. Target Discrimination • Operation of algorithm

34. Target Discrimination • Operation of algorithm cont

35. Target Discrimination • Operation of algorithm cont Jumping Trial

36. Target Discrimination • Operation of algorithm cont

37. Target Discrimination • Occluded targets

38. Conclusion • Centroid algorithm • Kalman filter • Model • Discrimination

39. Future Work • Hardware implementation • 3D application • Other biomechanical target discrimination (segmentation, etc.) • Other tracking application (space, robotics, etc.)