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

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Presentation on theme: "Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm and Maximum Likelihood Estimation by James Dennis Musick."— Presentation transcript:

1 Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm and Maximum Likelihood Estimation by James 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

12 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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