The Visual Object Tracking VOT2016 Challenge Results

The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply prelearned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending The Visual Object Tracking VOT2016 challenge results 3 the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website .

Gao | Philip H. S. Torr | Noor M. Al-Shakarji | João F. Henriques | Choi | Pflugfelder | M. H. Khan | Madan Kumar Rapuru | José M. Martínez | Vedaldi | Memarmoghadam | Danelljan | Muhammad Haris Khan | A. Gupta | A. Vedaldi | A. Leonardis | Bohyung Han | Longyin Wen | Naiyan Wang | K. Mikolajczyk | M. Felsberg | Martin Danelljan | D. Yeung | Jiri Matas | H. Bischof | Siwei Lyu | R. Laganière | F. Khan | Andreas Robinson | Huchuan Lu | Y. Demiris | Luca Bertinetto | Jack Valmadre | Zejian Yuan | F. Porikli | T. Mauthner | R. Stolkin | Giorgio Roffo | Jianke Zhu | S. Melzi | Bernard Ghanem | Hongdong Li | Michael Arens | Siyi Li | Zhenyu He | T. Pridmore | J. Lang | Xin Li | M. Valstar | Michel | Changsheng Xu | Daijin Kim | Lijun Wang | Tianzhu Zhang | O. Miksik | Wenbo Li | Weiming Hu | M. Kristan | Tomás Vojír | R. Pflugfelder | G. Fernandez | Luka Cehovin | A. Petrosino | Jin Gao | Jingjing Xiao | Junliang Xing | M. Maresca | P. Yuen | Gustav Häger | A. Lukežič | Alireza Memarmoghadam | Álvaro García-Martín | Andrés Solís Montero | A. J. Ma | A. Varfolomieiev | A. Alatan | Aykut Erdem | Bin Liu | Brais Martínez | Chang-Ming Chang | Chong Sun | Dapeng Chen | Dawei Du | Deepak Mishra | Erhan Gundogdu | E. Erdem | Fei Zhao | F. Bunyak | Francesco Battistone | Gao Zhu | G. Subrahmanyam | G. Bastos | G. Seetharaman | H. Medeiros | H. Qi | Horst Possegger | Hyemin Lee | Hyeonseob Nam | I. Drummond | Jae-chan Jeong | J. Cho | Jae-Y. Lee | Jiayi Feng | J. Choi | Ji-Wan Kim | Jiyeoup Jeong | Jongwon Choi | Junyu Gao | K. Palaniappan | K. Lebeda | Ke Gao | Lei Qin | M. Poostchi | Matthias Mueller | Mengdan Zhang | Ming Tang | Mooyeol Baek | Nana Fan | Osman Akin | P. Moallem | P. Senna | Qingming Huang | Rafael Martin-Nieto | R. Pelapur | R. Bowden | Ryan Walsh | S. Krah | Shengkun Li | Shengping Zhang | Shizeng Yao | Simon Hadfield | S. Becker | S. Golodetz | S. Kakanuru | Sunglok Choi | Tao Hu | V. Santopietro | W. Hübner | X. Lan | Xiaomeng Wang | Yang Li | Yifan Wang | Yuankai Qi | Z. Cai | Zhan Xu | Zhizhen Chi | Andrés Solís Montero | Yeung | Naiyan | Wang | Andrea | Roman | Alireza | Aydin | Alatan | B. Liu | Dit-Yan | Horst | Possegger | Hyung Jin | Chang | Jin Young | Jun-yu | Krystian | Mikołajczyk | Martin | Valstar | Richard | Bowden | B. Sebastian | Krah | Ze-jian | Yuan | Junyu | O. Mikšík | Henry Medeiros | G. R. S. Subrahmanyam | H. Chang | Rafael Martín-Nieto | Mahdieh Poostchi

[1]  Matej Kristan,et al.  Deformable Parts Correlation Filters for Robust Visual Tracking , 2016, IEEE Transactions on Cybernetics.

[2]  Qi Tian,et al.  Geometric Hypergraph Learning for Visual Tracking , 2016, IEEE Transactions on Cybernetics.

[3]  Wolfgang Hübner,et al.  MAD for visual tracker fusion , 2016, Security + Defence.

[4]  A. Aydin Alatan,et al.  Spatial windowing for correlation filter based visual tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[5]  Bohyung Han,et al.  Modeling and Propagating CNNs in a Tree Structure for Visual Tracking , 2016, ArXiv.

[6]  Michael Felsberg,et al.  Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.

[7]  Qingming Huang,et al.  Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph , 2016, IEEE Transactions on Image Processing.

[8]  Aykut Erdem,et al.  Deformable part-based tracking by coupled global and local correlation filters , 2016, J. Vis. Commun. Image Represent..

[9]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[10]  Yiannis Demiris,et al.  Visual Tracking Using Attention-Modulated Disintegration and Integration , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Xiaogang Wang,et al.  STCT: Sequentially Training Convolutional Networks for Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Guna Seetharaman,et al.  Semantic Depth Map Fusion for Moving Vehicle Detection in Aerial Video , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[13]  Hongdong Li,et al.  Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Ales Leonardis,et al.  Robust visual tracking using template anchors , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[15]  Shuicheng Yan,et al.  NUS-PRO: A New Visual Tracking Challenge , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Luca Bertinetto,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Jiri Matas,et al.  Online adaptive hidden Markov model for multi-tracker fusion , 2015, Comput. Vis. Image Underst..

[20]  Jiri Matas,et al.  A Novel Performance Evaluation Methodology for Single-Target Trackers , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ales Leonardis,et al.  Visual Object Tracking Performance Measures Revisited , 2015, IEEE Transactions on Image Processing.

[22]  Simone Melzi,et al.  Online Feature Selection for Visual Tracking , 2016, BMVC.

[23]  Jiri Matas,et al.  Texture-Independent Long-Term Tracking Using Virtual Corners , 2016, IEEE Transactions on Image Processing.

[24]  Michael Felsberg,et al.  The Visual Object Tracking VOT2015 Challenge Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[25]  Michael Felsberg,et al.  Learning Spatially Regularized Correlation Filters for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Ming Tang,et al.  Multi-kernel Correlation Filter for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Robert Laganière,et al.  Scalable Kernel Correlation Filter with Sparse Feature Integration , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[28]  Xiaogang Wang,et al.  Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[29]  Marco Cristani,et al.  Infinite Feature Selection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[30]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[31]  Zhenyu He,et al.  The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results , 2016, ECCV Workshops.

[32]  Tony P. Pridmore,et al.  TRIC-track: Tracking by Regression with Incrementally Learned Cascades , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Francesco Solera,et al.  Towards the evaluation of reproducible robustness in tracking-by-detection , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[34]  Erik Blasch,et al.  Encoding color information for visual tracking: Algorithms and benchmark , 2015, IEEE Transactions on Image Processing.

[35]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Thomas Mauthner,et al.  In defense of color-based model-free tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Ales Leonardis,et al.  Single target tracking using adaptive clustered decision trees and dynamic multi-level appearance models , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Roman P. Pflugfelder,et al.  Clustering of static-adaptive correspondences for deformable object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Ming-Hsuan Yang,et al.  Long-term correlation tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Stefan Roth,et al.  MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking , 2015, ArXiv.

[41]  Abhinav Gupta,et al.  Transferring Rich Feature Hierarchies for Robust Visual Tracking , 2015, ArXiv.

[42]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[43]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

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

[45]  José María Martínez Sanchez,et al.  Single Object Long-term Tracker for Smart Control of a PTZ camera , 2014, ICDSC.

[46]  Matej Kristan,et al.  A Graphical Model for Rapid Obstacle Image-Map Estimation from Unmanned Surface Vehicles , 2014, ACCV.

[47]  Tony P. Pridmore,et al.  MTS: A Multiple Temporal Scale Tracker Handling Occlusion and Abrupt Motion Variation , 2014, ACCV.

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

[49]  Alfredo Petrosino,et al.  Clustering Local Motion Estimates for Robust and Efficient Object Tracking , 2014, ECCV Workshops.

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

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

[52]  David Zhang,et al.  Fast Visual Tracking via Dense Spatio-temporal Context Learning , 2014, ECCV.

[53]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[55]  Stefanos Zafeiriou,et al.  Incremental Face Alignment in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[56]  Ales Leonardis,et al.  Is my new tracker really better than yours? , 2014, IEEE Winter Conference on Applications of Computer Vision.

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

[58]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[59]  Michael Felsberg,et al.  Enhanced Distribution Field Tracking Using Channel Representations , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[60]  Jiri Matas,et al.  Long-Term Tracking through Failure Cases , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[61]  Nanning Zheng,et al.  Constructing Adaptive Complex Cells for Robust Visual Tracking , 2013, 2013 IEEE International Conference on Computer Vision.

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

[63]  Shengping Zhang,et al.  Sparse coding based visual tracking: Review and experimental comparison , 2013, Pattern Recognit..

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

[65]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[66]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognit. Lett..

[67]  Guna Seetharaman,et al.  Feature selection for appearance-based vehicle tracking in geospatial video , 2013, Defense, Security, and Sensing.

[68]  Ales Leonardis,et al.  Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[70]  Guna Seetharaman,et al.  Efficient GPU Implementation of the Integral Histogram , 2012, ACCV Workshops.

[71]  Jiri Matas,et al.  Tracking the Untrackable: How to Track When Your Object Is Featureless , 2012, ACCV Workshops.

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

[73]  Guna Seetharaman,et al.  Robust Orientation and Appearance Adaptation for Wide-Area Large Format Video Object Tracking , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[74]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[75]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[76]  Jae-Yeong Lee,et al.  Visual tracking by partition-based histogram backprojection and maximum support criteria , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[77]  Horst Bischof,et al.  Hough-based tracking of non-rigid objects , 2011, 2011 International Conference on Computer Vision.

[78]  Ling Shao,et al.  Recent advances and trends in visual tracking: A review , 2011, Neurocomputing.

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

[80]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, 2011 International Conference on Computer Vision.

[81]  Jiri Matas,et al.  Robustifying the Flock of Trackers , 2011 .

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

[83]  Rama Chellappa,et al.  Online Empirical Evaluation of Tracking Algorithms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[84]  Guna Seetharaman,et al.  Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video , 2010, 2010 13th International Conference on Information Fusion.

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

[86]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[88]  Guillermo Sapiro,et al.  Online dictionary learning for sparse coding , 2009, ICML '09.

[89]  Jing Zhang,et al.  Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[92]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[93]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[94]  J.M. Ferryman,et al.  PETS Metrics: On-Line Performance Evaluation Service , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[95]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[96]  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).

[97]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[98]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[99]  Jacques Verly,et al.  The State of the Art in Multiple Object Tracking Under Occlusion in Video Sequences , 2003 .

[100]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[101]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[102]  Jorge Nocedal,et al.  A trust region method based on interior point techniques for nonlinear programming , 2000, Math. Program..

[103]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

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

[105]  D. Shanno Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .