Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches

Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over the image, we exploit the motion trajectories to distinguish them from the background. Therefore, the visual object can be defined as the clustering of homo-trajectory patches, where a Hough voting-like scheme is employed to estimate the target state. Encouraging experimental results on a large set of sequences showed that the proposed approach is very effective and in comparison to the state-of-the-art trackers. The full source code of our implementation will be publicly available.

[1]  Alfredo Petrosino,et al.  MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning , 2013, ICIAP.

[2]  Michael R. Lyu,et al.  Nonrigid shape recovery by Gaussian process regression , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Huchuan Lu,et al.  Least Soft-Threshold Squares Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Michael Felsberg,et al.  Adaptive Color Attributes for Real-Time Visual Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Vincent Lepetit,et al.  Dominant orientation templates for real-time detection of texture-less objects , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Horst Bischof,et al.  Learning Features for Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Roman P. Pflugfelder,et al.  Consensus-based matching and tracking of keypoints for object tracking , 2014, IEEE Winter Conference on Applications of Computer Vision.

[8]  Horst Bischof,et al.  Hough-based tracking of non-rigid objects , 2013, Comput. Vis. Image Underst..

[9]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

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

[11]  Cordelia Schmid,et al.  Learning Color Names for Real-World Applications , 2009, IEEE Transactions on Image Processing.

[12]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Stan Z. Li,et al.  Structured Visual Tracking with Dynamic Graph , 2012, ACCV.

[14]  Chun Chen,et al.  A Convolutional Treelets Binary Feature Approach to Fast Keypoint Recognition , 2012, ECCV.

[15]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[16]  Michael R. Lyu,et al.  Progressive Finite Newton Approach To Real-time Nonrigid Surface Detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  David Zhang,et al.  Fast Tracking via Spatio-Temporal Context Learning , 2013, ArXiv.

[18]  Haibin Ling,et al.  Robust visual tracking using ℓ1 minimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Gordon Wyeth,et al.  SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights , 2012, 2012 IEEE International Conference on Robotics and Automation.

[21]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[22]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[23]  Gilad Lerman,et al.  Better Feature Tracking through Subspace Constraints , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

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

[26]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Philip H. S. Torr,et al.  Efficient online structured output learning for keypoint-based object tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[29]  Mei Han,et al.  Efficient hierarchical graph-based video segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[31]  渡辺 亮平,et al.  Sequential Monte Carlo , 2005, Nonlinear Time Series Analysis.

[32]  Junseok Kwon,et al.  Visual tracking decomposition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[33]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[34]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Jiri Matas,et al.  The Enhanced Flock of Trackers , 2014, Registration and Recognition in Images and Videos.

[36]  Jin Gao,et al.  Transfer Learning Based Visual Tracking with Gaussian Processes Regression , 2014, ECCV.

[37]  Ming-Hsuan Yang,et al.  Least Soft-thresold Squares Tracking , 2013 .

[38]  Junseok Kwon,et al.  Tracking by Sampling Trackers , 2011, 2011 International Conference on Computer Vision.

[39]  Junseok Kwon,et al.  Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[41]  Rynson W. H. Lau,et al.  Visual Tracking via Locality Sensitive Histograms , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[44]  J. Halton.  Sequential Monte Carlo , 1962, Mathematical Proceedings of the Cambridge Philosophical Society.

[45]  Lu Zhang,et al.  Preserving Structure in Model-Free Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  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.