Online Object Tracking: A Benchmark

Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.

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

[2]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[5]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[7]  Robert T. Collins,et al.  Mean-shift blob tracking through scale space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[8]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Robert B. Fisher,et al.  The PETS04 Surveillance Ground-Truth Data Sets , 2004 .

[10]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[11]  Shai Avidan,et al.  Support vector tracking , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[13]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[14]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[16]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Robert T. Collins,et al.  An Open Source Tracking Testbed and Evaluation Web Site , 2005 .

[19]  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).

[20]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[22]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[23]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[24]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[26]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[28]  Qi Zhao,et al.  Co-Tracking Using Semi-Supervised Support Vector Machines , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[29]  Kevin Cannons,et al.  A Review of Visual Tracking , 2008 .

[30]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[31]  Björn Stenger,et al.  Learning to track with multiple observers , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Serge J. Belongie,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Junseok Kwon,et al.  Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling , 2009, CVPR.

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

[35]  Luc Van Gool,et al.  Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[36]  Gang Hua,et al.  Context-Aware Visual Tracking , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Horst Bischof,et al.  PROST: Parallel robust online simple tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[39]  Nassir Navab,et al.  Rapid selection of reliable templates for visual tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  Philippe C. Cattin,et al.  Tracking the invisible: Learning where the object might be , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[41]  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.

[42]  Ying Wu,et al.  Discriminative Spatial Attention for Robust Tracking , 2010, ECCV.

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

[44]  Feng Li,et al.  Blurred target tracking by Blur-driven Tracker , 2011, 2011 International Conference on Computer Vision.

[45]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Junzhou Huang,et al.  Robust tracking using local sparse appearance model and K-selection , 2011, CVPR 2011.

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

[48]  Gérard G. Medioni,et al.  Context tracker: Exploring supporters and distracters in unconstrained environments , 2011, CVPR 2011.

[49]  Bin Shen,et al.  Online robust image alignment via iterative convex optimization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[50]  Laura Sevilla-Lara,et al.  Distribution fields for tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[53]  Narendra Ahuja,et al.  Robust visual tracking via multi-task sparse learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[55]  Shai Avidan,et al.  Locally Orderless Tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[56]  Narendra Ahuja,et al.  Robust Visual Tracking via Structured Multi-Task Sparse Learning , 2012, International Journal of Computer Vision.

[57]  Li Bai,et al.  Real-Time Probabilistic Covariance Tracking With Efficient Model Update , 2012, IEEE Transactions on Image Processing.

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

[59]  Ming Tang,et al.  Robust tracking via weakly supervised ranking SVM , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[60]  Ying Wu,et al.  Scribble Tracker: A Matting-Based Approach for Robust Tracking , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[61]  Kuk-Jin Yoon,et al.  Visual Tracking via Adaptive Tracker Selection with Multiple Features , 2012, ECCV.

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

[63]  Zhongfei Zhang,et al.  A survey of appearance models in visual object tracking , 2013, ACM Trans. Intell. Syst. Technol..

[64]  Huchuan Lu,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 TRANSACTIONS ON IMAGE PROCESSING 1 Online Object Tracking with Sparse Prototypes , 2022 .

[65]  Li Bai,et al.  Efficient Minimum Error Bounded Particle Resampling L1 Tracker With Occlusion Detection , 2013, IEEE Transactions on Image Processing.