Discriminative Correlation Filter 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 novel 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 allows tracking of non-rectangular objects as well as extending the search region. Channel reliability reflects the quality of the learned filter and it is used as a feature weighting coefficient in localization. Experimentally, with only two simple standard features, 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 OTB. The CSR-DCF runs in real-time on a CPU.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[26]  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).

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

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

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

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

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

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

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

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

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

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

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

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

[40]  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).

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

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

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

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