Target Tracking a Non-Linear Target Path

Using Kalman Predictive Algorithm and Maximum

Likelihood Estimationby

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

• 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

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Kalman Filter cont.

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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

• 3D application

• Other biomechanical target discrimination (segmentation, etc.)

• Other tracking application (space, robotics, etc.)