Discriminative Correlation Filter Tracker with Channel and Spatial Reliability

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method—DCF with channel and spatial reliability—achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.

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

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

[3]  Theo Gevers,et al.  A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation , 2007, IEEE Transactions on Neural Networks.

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

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

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

[7]  Qingming Huang,et al.  Hedged Deep Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Matej Kristan,et al.  Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles , 2015, IEEE Transactions on Cybernetics.

[9]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

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

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

[12]  Gang Wang,et al.  Real-time part-based visual tracking via adaptive correlation filters , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

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

[16]  Jiri Matas,et al.  Pixel-Wise Object Segmentations for the VOT 2016 Dataset , 2017 .

[17]  Michael Felsberg,et al.  The Visual Object Tracking VOT2013 Challenge Results , 2013, ICCV 2013.

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

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

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

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

[22]  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).

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

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

[25]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[26]  Changsheng Xu,et al.  Structural Correlation Filter for Robust Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Simon Lucey,et al.  Multi-channel Correlation Filters , 2013, 2013 IEEE International Conference on Computer Vision.

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

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

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

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

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

[33]  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).

[34]  Zhenyu He,et al.  The Visual Object Tracking VOT2016 Challenge Results , 2016, ECCV Workshops.

[35]  Rustam,et al.  The Visual Object Tracking VOT 2013 challenge results , 2018 .

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

[37]  Michael Felsberg,et al.  The Visual Object Tracking VOT2013 Challenge Results , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

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

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

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

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

[42]  Wei Liu,et al.  Visual Tracking with Reliable Memories , 2016, IJCAI.

[43]  Michael Felsberg,et al.  Discriminative Scale Space Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  D Casasent,et al.  Multivariant technique for multiclass pattern recognition. , 1980, Applied optics.

[45]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, 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]  Zhe Chen,et al.  MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[50]  Simon Lucey,et al.  Correlation filters with limited boundaries , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

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

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

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

[56]  Michael Felsberg,et al.  Convolutional Features for Correlation Filter Based Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).