Object tracking with mask-constrained spatial context

Discriminative correlation filters (DCFs) have shown excellent performance in visual tracking. DCF substitutes the sliding windows sampling strategy in traditional tracking methods with circular shift of the context area. Via projecting the filter learning into the frequency domain, DCF achieves satisfying performance and speed. Appropriate context area size has an influence on the performance of correlation filters. Small context area limits the CF’s ability to handle fast motion and partial occlusion, whereas large context area leads the CF to suffer from boundary effect. To make use of a large area of context and alleviate the accompanying drift risk, we propose a mask-constrained context correlation filter for object tracking. We first analyze the traditional window strategy via Taylor series and design a spatial mask that can be covered by a larger context area. Furthermore, the shape of the mask is adaptive to the target variation. Extensive experimental results in OTB-2015, VOT-2014, and VOT-2016 datasets demonstrate that this mask-constrained operation can improve the CF tracker performance in a large margin. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.6.063007]

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

[2]  Seunghoon Hong,et al.  Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network , 2015, ICML.

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

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

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

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

[7]  Hong Huo,et al.  Learning channel-aware deep regression for object tracking , 2019, Pattern Recognit. Lett..

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

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

[10]  Luca Bertinetto,et al.  End-to-End Representation Learning for Correlation Filter Based Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[12]  Xiao Wang,et al.  SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[14]  Xiaochun Cao,et al.  Fusing two-stream convolutional neural networks for RGB-T object tracking , 2017, Neurocomputing.

[15]  Ling Shao,et al.  Submodular Trajectories for Better Motion Segmentation in Videos , 2018, IEEE Transactions on Image Processing.

[16]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[18]  Le Zhang,et al.  Robust visual tracking via co-trained Kernelized correlation filters , 2017, Pattern Recognit..

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

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

[21]  Liang Lin,et al.  Visual Tracking via Dynamic Graph Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Jin Tang,et al.  RGB-T Object Tracking: Benchmark and Baseline , 2018, Pattern Recognit..

[23]  Stan Z. Li,et al.  Online Spatio-temporal Structural Context Learning for Visual Tracking , 2012, ECCV.

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

[25]  Bernard Ghanem,et al.  Context-Aware Correlation Filter Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Quang Tran,et al.  Robust Visual Tracking Using Randomized Forest and Online Appearance Model , 2011, ACIIDS.

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

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

[29]  Wenguan Wang,et al.  Occlusion-Aware Real-Time Object Tracking , 2017, IEEE Transactions on Multimedia.

[30]  Bingbing Ni,et al.  Deep Regression Tracking with Shrinkage Loss , 2018, ECCV.

[31]  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).

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

[33]  Jianbing Shen,et al.  Fast Online Tracking With Detection Refinement , 2018, IEEE Transactions on Intelligent Transportation Systems.

[34]  Yong Liu,et al.  Large Margin Object Tracking with Circulant Feature Maps , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Yang Li,et al.  Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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