Ultra-fast tracking based on zero-shift points

A novel tracker based on points where the intensity function is locally even is presented. Tracking of these so called zero-shift points (ZSPs) is very efficient, a single point is tracked on average in less than 10 microseconds on a standard notebook. We demonstrate experimentally the robustness of the tracker to image transformations and a relatively long lifetime of ZSPs in real videosequences.

[1]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[2]  Jiri Matas,et al.  Optimal Randomized RANSAC , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[6]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[7]  Václav Hlavác,et al.  Stable Wave Detector of Blobs in Images , 2006, DAGM-Symposium.

[8]  David W. Murray,et al.  Video-rate localization in multiple maps for wearable augmented reality , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[9]  Nassir Navab,et al.  A dataset and evaluation methodology for template-based tracking algorithms , 2009, 2009 8th IEEE International Symposium on Mixed and Augmented Reality.

[10]  Jiri Matas,et al.  Tracking by an Optimal Sequence of Linear Predictors , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jiri Matas,et al.  Ultra-fast tracking based on zero-shift points , 2011, ICASSP.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Hans P. Morevec Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[16]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[17]  Jiri Matas,et al.  Locally Optimized RANSAC , 2003, DAGM-Symposium.

[18]  Nassir Navab,et al.  Adaptive linear predictors for real-time tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Michel Dhome,et al.  Real Time Robust Template Matching , 2002, BMVC.

[20]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[21]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  M. F.,et al.  Bibliography , 1985, Experimental Gerontology.