Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm by James Dennis Musick

Agenda Introduction Problem Definition Centroid Algorithm 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 –Centroid calculation –Prediction –Discrimination

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

Centroid Algorithm Introduction Scanning scheme

Centroid Algorithm cont. 640 x 480 –~ pixels 8-bit Gray-scale Block diagram ThresholdX/Y address location Target Discrimination Buffer Logic control and centroid calculation Centroid Value Memory

Centroid Algorithm cont. Threshold

Centroid Algorithm cont. x/y addressing

Centroid Algorithm cont. Target Pixel Discrimination Buffer –x_sum, y_sum, LS_target, RS_target, Bot_target, target_pixel_num

Centroid Algorithm cont. Logic Control and Centroid Calculation

Centroid Algorithm cont. Centroid Memory Buffer –Once a target is completed (defined as no pixels within the search criteria at the row just below the target), then the centroid data is stored in a memory array until the data is read out at the end of the number of pictures that are being analyzed. –The array would be structured in the following manner if there were three targets in each of 5 pictures: Target_Centroid_Array = (xy,Target #, Picture #) => (1:2, 1:3, 1:5).

Centroid Algorithm cont. Examples

Centroid Algorithm cont. Performance and Limitations –Three targets simultaneous –Total number

Centroid Algorithm cont. 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.

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

Operation of the Kalman Filter

Kalman Filter cont Operation of the Kalman Filter

Kalman Filter cont Operation of the Kalman Filter

Kalman Filter cont Operation of the Kalman Filter

Kalman Filter cont Operation of the Kalman Filter

Kalman Filter cont Operation of the Kalman Filter

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

Target Discrimination Occluded targets

Conclusion Centroid algorithm Kalman filter –Model Discrimination

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