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

Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm. by James Dennis Musick. Agenda. Introduction Problem Definition Centroid Algorithm Kalman Filter Target Discrimination Conclusion Future Work. Introduction.

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

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  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.)

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