Interest points based object tracking via sparse representation

In this paper, we propose an interest point based object tracker in sparse representation (SR) framework. In the past couple of years, there have been many proposals for object tracking in sparse framework exhibiting robust performance in various challenging scenarios. One of the major issues with these SR trackers is its slow execution speed mainly attributed to the particle filter framework. In this paper, we propose a robust interest point based tracker in l1 minimization framework that runs at real-time with better performance compared to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points are obtained via solving the proposed l1 minimization problem. A robust matching criterion is proposed to prune the noisy matches. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance of the proposed tracker is bench marked with several complex video sequences and found to be fast and robust compared to reported state of the art trackers.

[1]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

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

[4]  R. Venkatesh Babu,et al.  Local appearance based robust tracking via sparse representation , 2012, ICVGIP '12.

[5]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  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).

[7]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[8]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[11]  Li Bai,et al.  Minimum error bounded efficient ℓ1 tracker with occlusion detection , 2011, CVPR 2011.

[12]  Anamitra Makur,et al.  Online adaptive radial basis function networks for robust object tracking , 2010, Comput. Vis. Image Underst..

[13]  Rama Chellappa,et al.  Object Detection, Tracking and Recognition for Multiple Smart Cameras , 2008, Proceedings of the IEEE.

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

[15]  Martin Kampel,et al.  Interest Point Based Tracking , 2010, 2010 20th International Conference on Pattern Recognition.

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

[17]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Patrick Pérez,et al.  Robust tracking with motion estimation and local Kernel-based color modeling , 2007, Image Vis. Comput..

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

[20]  Janusz Konrad,et al.  Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[21]  Fuchun Sun,et al.  Visual Tracking Using Sparsity Induced Similarity , 2010, 2010 20th International Conference on Pattern Recognition.

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

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

[24]  Irene Yu-Hua Gu,et al.  Chapter 5: Online Learning and Robust Visual Tracking using Local Features and Global Appearances of Video Objects , 2011 .

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

[26]  R. Venkatesh Babu,et al.  Fragment-based real-time object tracking: A sparse representation approach , 2012, 2012 19th IEEE International Conference on Image Processing.

[27]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.