1. Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm by James 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. Centroid Algorithm cont. 640 x 480 –~ 307200 pixels 8-bit Gray-scale Block diagram ThresholdX/Y address location Target Discrimination Buffer Logic control and centroid calculation Centroid Value Memory

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

20. 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. 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.)