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by James Dennis Musick

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

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by James Dennis Musick

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

  2. Agenda • Introduction • Problem Definition • 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 • Path Prediction • Discrimination

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

  6. Video Target Identification • Threshold

  7. Target Algorithm Uncertainty • 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)

  8. Kalman Filter • Introduction • State Space representation

  9. Kalman Filter cont.

  10. Kalman Filter cont

  11. Kalman Filter cont

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

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

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

  15. Kalman Filter cont • b

  16. Kalman Filter Noisy Acceleration • Operation of the Kalman Filter

  17. Kalman Filter Noisy Acceleration • Operation of the Kalman Filter

  18. Kalman Filter Noisy Acceleration • Operation of the Kalman Filter

  19. Kalman Filter Noisy Jerk • Operation of the Kalman Filter

  20. Kalman Filter Noisy Jerk • Operation of the Kalman Filter

  21. Kalman Filter Noisy Jerk • Operation of the Kalman Filter

  22. Kalman Filter • Occluded targets

  23. Target Discrimination • Introduction • Goal

  24. Target Discrimination • Example

  25. Target Discrimination • Example cont

  26. Target Discrimination • Operation of algorithm

  27. Target Discrimination • Operation of algorithm cont

  28. Target Discrimination • Operation of algorithm cont Jumping Trial

  29. Target Discrimination • Operation of algorithm cont

  30. Conclusion • Kalman filter • Model • Discrimination

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

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