Object tracking via adaptive prediction of initial search point on mobile devices TJ Melanson Department of Electrical Engineering, Stanford University Motivation Adaptive Prediction Technique References Experimental Results • Common feature tracking algorithms, such as SIFT and SURF, are fairly slow in runtime due to the processing of a large amount of external data. • If the object is sufficiently small, outside noise may throw off the object detection device without prior knowledge. • Machine learning, especially Markov chains, can use prior knowledge to turn a computationally expensive task into a faster, stochastic one. • http://www.cs.cmu.edu/~jiyanpan/papers/lncs06.pdf - Inspiration for this project, uses Kalman filter on the rate of change of affine transformation parameters for adaptive prediction • Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985) • Andrew W. Fitzgibbon, R.B.Fisher. A Buyer’s Guide to Conic Fitting. Proc.5th British Machine Vision Conference, Birmingham, pp. 513-522, 1995. Above two links outline the contour and conic detection methods used to determine the region outline Contour/Bound detec-on Homography Es-ma-on (ORB Keypoints) Hough Line Detec-on, Bounding Box est. Input binary image Comparison with Predic-on Predict Object Parameters Update Current State Es-ma-on • Creating a better template for homography estimation than the first image, which will reduce noise in deteting keypoints • Generalized algorithm so a template can be chosen on Android without further tuning • Further integration of the masking region Future Work Left: the Kalman filtered box estimation (blue) is more invariant to size and horizontal shifts in position than the standard detector (magenta) Top: The descriptor matches contain much less noise with the region masking (left) than without any region masking (right).
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Object tracking via adaptive prediction of initial search point on mobile devicesTJ Melanson
Department of Electrical Engineering, Stanford University
Motivation Adaptive Prediction Technique
References
Experimental Results
• Common feature tracking algorithms, such as SIFT and SURF, are fairly slow in runtime due to the processing of a large amount of external data.
• If the object is sufficiently small, outside noise may throw off
the object detection device without prior knowledge. • Machine learning, especially Markov chains, can use prior
knowledge to turn a computationally expensive task into a faster, stochastic one.
• http://www.cs.cmu.edu/~jiyanpan/papers/lncs06.pdf - Inspiration for this project, uses Kalman filter on the rate of change of affine transformation parameters for adaptive prediction
• Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985)
• Andrew W. Fitzgibbon, R.B.Fisher. A Buyer’s Guide to Conic Fitting. Proc.5th British Machine Vision Conference, Birmingham, pp. 513-522, 1995. Above two links outline the contour and conic detection methods used to determine the region outline
Contour/Bounddetec-on
HomographyEs-ma-on(ORB
Keypoints)
HoughLineDetec-on,
BoundingBoxest.
Inputbinaryimage
ComparisonwithPredic-on
PredictObjectParameters
UpdateCurrentStateEs-ma-on
• Creating a better template for homography estimation than the first image, which will reduce noise in deteting keypoints
• Generalized algorithm so a template can be chosen on
Android without further tuning
• Further integration of the masking region
Future Work
Left: the Kalman filtered box estimation (blue) is more invariant to size and horizontal shifts in position than the standard detector (magenta) Top: The descriptor matches contain much less noise with the region masking (left) than without any region masking (right).
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