Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm by James Dennis Musick.
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Transcript of Target Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm by James Dennis Musick.
Target Tracking a Non-Linear Target Path
Using Kalman Predictive Algorithm
byJames 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– ~ 307200 pixels
• 8-bit Gray-scale
• Block diagramThreshold X/Y
address location
Target Discrimination Buffer
Logic control and centroid calculation
Centroid ValueMemory
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
}iin target pixels{
}iin target pixels{
}iin target pixels{
}iin target pixels{ ,),(
kk
kk
kk
kk
iii n
y
n
x
Cyx
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
kkk xTxx 1
Velocityxk
k
k
k
k
x
xT
x
x
10
1
1
1
Kalman Filter cont.
kkkk xT
xTxx 2
1 2 wk ux
T
2
2
wkkk uxTxx 1 Velocityxk onAcceleratixk
wkk BuApp 1
vkk DuCps ),0(~ 2ww Nu ),0(~ 2
vv Nu
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 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
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.)