An Information Fusion Approach for Multiview Feature Tracking Esra Ataer-Cansizoglu...
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An Information Fusion Approach for Multiview Feature Tracking Esra Ataer-Cansizoglu ([email protected]) and Margrit Betke ([email protected] ) Image and Video Computing Group, Computer Science Department, Boston University Introduction Where is the object/feature point? time Robust tracking is important for Human Computer Interaction Video-based Surveillance Remote Sensing Video Indexing Problem: Failure in tracking, especially due to occlusion Solution: Automatic Recovery Idea: Use multiple cameras http://www.cs.bu.edu/faculty/betke/research/jordan- bubble.jpg Conclusion System detects tracking failures with high accuracy Promising results on automatic feature re- initialization Correlation based term is strong to predict reliability Proposed RM is inexpensive to compute Feature Extensions: Use of particle filters or other trackers on 3D Extend RM using geometric constraints about the motion of the object Use of multiple points considering constraints about shape Proposed Method Idea: Construct a Reliability Measure (RM) for each view to detect tracking failures Observation: Prefer the view in which object is most visible. Which parameter is most informative for tracking in a view? 2 , , 2 2 , , 2 , , , ) , ( ) , ( ) , ( ) , ( ) , ( ) , ( ) , ( ) , ( ) , ( y x y x y x y x y x y x y x y x T y x T N y x I y x I N y x T y x I y x T y x I N T I NCC 0 R T L Fx x Wikipedia, Epipolar Geometry, http://en.wikipedia.org/wiki/Epipolar_geometry F: Fundamental Matrix Right Camera Left Camera Normalized Correlation Coefficient (NCC) I: Image T: Template N: number of pixels Epipolar distance (EPD) Estimate of the 3D Position Geometric constraints about the shape/motion of the object )) ( ( z NCC U ) ' , ( ) ' , ( z z EPD z y EPD U s y z U )) ( ( 1 [ y NCC U Term 1 Term 2 Term 3 Term 4 z: 2D tracks, : Reconstructed 3D Trajectory, y: Projection of estimated 3D position 1 0 0 , 1 , , 0 ) ( x x otherwise if if x x U 1 4 1 i i and Term 2 Term 3 Term 4 Term 1 ) ' , ( ) ' , ( ))] ( ( 1 [ )) ( ( 4 3 2 1 z z EPD z y EPD U threshold y z U y NCC U z NCC U RM where 2 1 2 t t t X X X Proposed System: Independent 2D trackers in each view utilize pyramidal implementation of optical flow algorithm Stereoscopic reconstruction of 3D trajectories (simple linear method) Predicting 3D Position with ‘Constant Velocity Assumption’ Automatic recovery using projection of estimated 3D position 0 1 Experiments & Results Left-view RM (top) and right-view RM’ (bottom) for the video of subject A and direction of subject’s movements where 1 i 0 j i j for all and High correlation b/w values of term 1 and term 2 DROP TERM 2! RMs with do not have succinct peaks. Weigh term 1 more! 1 0 Values of pairs and triplets of RM terms with weights set equally. RMs with final weights for subject A. Final Weights 5 . 0 1 0 2 25 . 0 4 3 DATASET Cameras 20 inches apart, 120 o between optical axis Training Set: 8 subjects, ~450 frames each Subjects rotating head center to right and then left and up, down. Test Set: 26 subjects, 2 sequences per subject, ~1200 frames in each sequence. Recording data from left and right cameras, while subject is using a mouse-replacement interface from frontal camera RESULTS The feature was lost in both views 9 times, but was declared as lost in only one of the views. 53 false alarms, but in all cases the feature was reinitialized to a location at most 3 pixels from the actual location, hence the false alarm rate is negligible. For 254 correctly detected tracking failures, the system was able to recover 181 times (71.3%). 304 feature loss events in one view = 254 detected in correct view + 25 detected in the other view + 25 not detected True positive rate 83.5% Feature Loss Problem with Camera Mouse: Camera Mouse is a mouse-replacement interface for people with disabilities. Automatic re-initialization would enable the subject to use Camera Mouse freely without the intervention of a caregiver. Adjusting Weights X ˆ References [1] Camera Mouse, http://www.cameramouse.org/, accessed August 2010. [2] C. Connor, E. Yu, J. Magee, E. Cansizoglu, S. Epstein, and M. Betke, "Movement and Recovery Analysis of a Mouse-Replacement Interface for Users with Severe Disabilities," 13th Int. Conference on Human-Computer Interaction, 10 pp., San Diego, USA, July 2009. [3] Y. Tong, Y. Wang, Z. Zhu, and Q. Ji, “Robust Facial Feature Tracking under Varying Face Pose and Facial Expression,” Pattern Recognition, 40(11):3195-3208, November 2007. [4] C. Fagiani, M. Betke, and J. Gips, “Evaluation of tracking methods for human-computer interaction,” IEEE Workshop on Applications in Computer Vision (WACV 2002), pp. 121-126, Orlando, USA, December 2002.
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Transcript of An Information Fusion Approach for Multiview Feature Tracking Esra Ataer-Cansizoglu...
An Information Fusion Approach for Multiview Feature Tracking
Esra Ataer-Cansizoglu ([email protected]) and Margrit Betke ([email protected] ) Image and Video Computing Group, Computer Science Department, Boston University
Introduction
Where is the object/feature point?
time
Robust tracking is important for
Human Computer Interaction
Video-based Surveillance
Remote Sensing
Video Indexing
Problem: Failure in tracking,
especially due to occlusion
Solution:
Automatic Recovery
Idea: Use multiple cameras
http://www.cs.bu.edu/faculty/betke/research/jordan-bubble.jpg
Conclusion System detects tracking failures with high accuracy
Promising results on automatic feature re-initialization
Correlation based term is strong to predict reliability
Proposed RM is inexpensive to compute
Feature Extensions:
Use of particle filters or other trackers on 3D
Extend RM using geometric constraints about the motion of the object
Use of multiple points considering constraints about shape
Proposed Method
Idea: Construct a Reliability Measure (RM)for each view to detect tracking failures
Observation: Prefer the view in which object is most visible.
Which parameter is most informative for tracking in a view?
2
,,
2
2
,,
2
, ,,
),(),(),(),(
),(),(),(),(
),(
yxyxyxyx
yx yxyx
yxTyxTNyxIyxIN
yxTyxIyxTyxIN
TINCC
0RTL Fxx
Wikipedia, Epipolar Geometry, http://en.wikipedia.org/wiki/Epipolar_geometry
F: Fundamental Matrix
Right CameraLeft Camera
Normalized Correlation Coefficient (NCC)I: ImageT: TemplateN: number of pixels
Epipolar distance (EPD)
Estimate of the 3D Position
Geometric constraints about the shape/motion of the object
))(( zNCCU
)',(
)',(
zzEPD
zyEPDU
s
yzU
))]((1[ yNCCU
Term 1
Term 2
Term 3
Term 4
z: 2D tracks, : Reconstructed 3D Trajectory, y: Projection of estimated 3D position
10
0
,1
,
,0
)( x
x
otherwise
if
if
xxU 14
1
i
i and
Term 2 Term 3 Term 4Term 1
)',(
)',())]((1[))(( 4321 zzEPD
zyEPDU
threshold
yzUyNCCUzNCCURM
where
212 ttt XXX
Proposed System:
Independent 2D trackers in each view utilize pyramidal implementation of optical flow algorithm
Stereoscopic reconstruction of 3D trajectories (simple linear method)
Predicting 3D Position with ‘Constant Velocity Assumption’
Automatic recovery using projection of estimated 3D position
01
Experiments & Results
Left-view RM (top) and right-view RM’ (bottom) for the video of subject A and direction of subject’s movements where 1i 0j ij for alland
High correlation b/w values of
term 1 and term 2
DROP TERM 2!
RMs with do not have
succinct peaks.Weigh term 1 more!
1 0
Values of pairs and triplets of RM terms with weights set equally. RMs with final weights for subject A.
Final Weights5.01
02
25.043
DATASET
Cameras 20 inches apart, 120o between optical axis Training Set: 8 subjects, ~450 frames each Subjects rotating head center to right and then left and up, down.
Test Set: 26 subjects, 2 sequences per subject, ~1200 frames in each sequence.
Recording data from left and right cameras, while subject is using a mouse-replacement interface from frontal camera
RESULTS
The feature was lost in both views 9 times, but was declared as lost in only one of the views.
53 false alarms, but in all cases the feature was reinitialized to a location at most 3 pixels from the actual location, hence the false alarm rate is negligible.
For 254 correctly detected tracking failures, the system was able to recover 181 times (71.3%).
304 feature loss events in one view
=254 detected in
correct view +25 detected in the other view + 25 not detected
True positive rate 83.5%
Feature Loss Problem with Camera Mouse: Camera Mouse is a mouse-replacement interface
for people with disabilities. Automatic re-initialization would enable the subject
to use Camera Mouse freely without the intervention of a caregiver.
Adjusting Weights
X̂
References[1] Camera Mouse, http://www.cameramouse.org/, accessed August 2010.
[2] C. Connor, E. Yu, J. Magee, E. Cansizoglu, S. Epstein, and M. Betke, "Movement and Recovery Analysis of a Mouse-Replacement Interface for Users with Severe Disabilities," 13th Int. Conference on Human-Computer Interaction, 10 pp., San Diego, USA, July 2009.
[3] Y. Tong, Y. Wang, Z. Zhu, and Q. Ji, “Robust Facial Feature Tracking under Varying Face Pose and Facial Expression,” Pattern Recognition, 40(11):3195-3208, November 2007.
[4] C. Fagiani, M. Betke, and J. Gips, “Evaluation of tracking methods for human-computer interaction,” IEEE Workshop on Applications in Computer Vision (WACV 2002), pp. 121-126, Orlando, USA, December 2002.
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