Target Tracking a NonLinear Target Path Using Kalman

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 • • 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 2 D Analysis 3 D 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) Blue missing (3. 5, 4) Red missing (3. 64, 4. 21) Correct (3. 5, 3) Red missing (3. 8, 3. 17)

Kalman Filter • Introduction – State Space representation

Kalman Filter cont.

Kalman Filter cont

Kalman Filter cont

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 cont • b

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 • 3 D application • Other biomechanical target discrimination (segmentation, etc. ) • Other tracking application (space, robotics, etc. )