Automatic Facial Landmark Tracking in Video Sequences using Kalman Filter Assisted Active Shape...
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Transcript of Automatic Facial Landmark Tracking in Video Sequences using Kalman Filter Assisted Active Shape...
Automatic Facial Landmark Tracking in Video Sequences using Kalman Filter Assisted Active Shape Models
Utsav Prabhu, Keshav Seshadri, Marios Savvides
報告人 : 李治衡 2011/06/08
Outline• Background
- ASM
- Kalman Filter • Tracking Methods
- Purely ASM
- Kalman Filter Assisted ASM• Experiments and Results• Conclusions and Future Work
Background – ASM Active Shape Model
1) Generate facial model using training images
2) Detect face in test image
3) Deform model to fit face in test image
Background – ASM • Active Shape Model
- Any Facial Shape
- Mean Shape
P - Eigenvector matrix
b - Projection coefficients
Background – Kalman FilterEstimate optimal state at time t
with a measurement given by
Prediction Stage
Correction Stage
Tracking MethodsPurely ASM Based Approaches• ASM on individual frames• ASM on individual frames with correction• ASM with initialization using previous frame
Kalman Filter Assisted ASM• Tracking landmark coordinates across frames• Tracking parameters that affect landmark positions
Tracking Methods – Kalman Filter Assisted
Experiments and Results
(a) (b) (c)
(a) Initialization provided by face detection(b) Initialization provided by using ASM results of previous frame(c) Initialization provided by prediction step of Kalman filter
Experiments and Results
(a) (b) (c) (d) (e)
(a) ASM on individual frames(b) ASM on individual frames with correction(c) ASM initialized using results of previous frame(d) ASM with Kalman filtering of landmark coordinates(e) ASM with Kalman filtering of parameters affecting landmark locations
Methods Result Comparison
Experiments and Results
Conclusion & Future Work • Experiments on 3 videos confirm our Kalman based
approaches enable better ASM initialization and lower
fitting errors• Background subtraction and re‐initialization of ASM to deal
with scene changes, zooming in of subject etc.• Speed optimizations for our ASM and Kalman tracking
implementations• Benchmark our approach on publicly available datasets/more
challenging datasets
Thank you for your attention!