TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications.

[1]  Zhanyi Hu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Rotationally Invariant Descript , 2011 .

[2]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Cordelia Schmid,et al.  Action recognition by dense trajectories , 2011, CVPR 2011.

[4]  Amy Loutfi,et al.  Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision , 2016 .

[5]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[6]  Vincent Lepetit,et al.  Efficient Discriminative Projections for Compact Binary Descriptors , 2012, ECCV.

[7]  Eli Shechtman,et al.  Matching Local Self-Similarities across Images and Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Krystian Mikolajczyk,et al.  Evaluation of local detectors and descriptors for fast feature matching , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[9]  Florian Baumann,et al.  Motion Binary Patterns for Action Recognition , 2014, ICPRAM.

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

[11]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Tal Hassner,et al.  Motion Interchange Patterns for Action Recognition in Unconstrained Videos , 2012, ECCV.

[13]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Guillaume-Alexandre Bilodeau,et al.  Efficient Action Recognition with MoFREAK , 2013, 2013 International Conference on Computer and Robot Vision.

[15]  Jiwen Lu,et al.  Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[17]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[18]  Christopher Joseph Pal,et al.  Activity recognition using the velocity histories of tracked keypoints , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Jintao Li,et al.  Hierarchical spatio-temporal context modeling for action recognition , 2009, CVPR.

[20]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[22]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[23]  Bernd Girod,et al.  CHoG: Compressed histogram of gradients A low bit-rate feature descriptor , 2009, CVPR.

[24]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[25]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Luc Van Gool,et al.  An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.

[27]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Prosenjit Bose,et al.  Global Context Descriptors for SURF and MSER Feature Descriptors , 2010, 2010 Canadian Conference on Computer and Robot Vision.

[29]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Mubarak Shah,et al.  Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Cordelia Schmid,et al.  Actions in context , 2009, CVPR.