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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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Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm and Maximum Likelihood Estimation byJames Dennis Musick
Agenda • Introduction • Problem Definition • Kalman Filter • Target Discrimination • Conclusion • Future Work
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
Problem Definition • In order to track the following have to be accomplished • Path Prediction • Discrimination
Problem Definition cont. • Trials used • Walking Trial • Jumping Trial • Waving Wand Trial • Increasing complexity
Video Target Identification • Threshold
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)
Kalman Filter • Introduction • State Space representation
Kalman Filter cont • Target Models: • Noisy Acceleration model
Kalman Filter cont • Target Models: • Noisy Jerk model
Kalman Filter cont • Selection of update time: • T = 1
Kalman Filter Noisy Acceleration • Operation of the Kalman Filter
Kalman Filter Noisy Acceleration • Operation of the Kalman Filter
Kalman Filter Noisy Acceleration • Operation of the Kalman Filter
Kalman Filter Noisy Jerk • Operation of the Kalman Filter
Kalman Filter Noisy Jerk • Operation of the Kalman Filter
Kalman Filter Noisy Jerk • Operation of the Kalman Filter
Kalman Filter • Occluded targets
Target Discrimination • Introduction • Goal
Target Discrimination • Example
Target Discrimination • Example cont
Target Discrimination • Operation of algorithm
Target Discrimination • Operation of algorithm cont
Target Discrimination • Operation of algorithm cont Jumping Trial
Target Discrimination • Operation of algorithm cont
Conclusion • Kalman filter • Model • Discrimination
Future Work • Hardware implementation • 3D application • Other biomechanical target discrimination (segmentation, etc.) • Other tracking application (space, robotics, etc.)