Context-learning correlation filters for long-term visual tracking

Correlation Filters (CFs) based trackers have recently attracted many researchers’ attention because of their high efficiency and robustness. Nevertheless, CFs trackers usually require a cosine window on account of the boundary effects. This allows trackers to distinguish targets in small background areas. In this paper, we propose an online learning algorithm that employs the global context to alleviate the problems. It is based on Passive-Aggressive algorithm that incorporates context information within CFs trackers. In addition, we train an SVM classifier to redetect objects in case of the model drift caused by occlusion and fast motion etc. The results of extensive experiments on a large-scale benchmark dataset show that the proposed tracker outperform the state-of-the-art trackers.

[1]  Ding Yuan,et al.  Infrared small target detection based on local intensity and gradient properties , 2018 .

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

[3]  Michael Felsberg,et al.  Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[6]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Cordelia Schmid,et al.  Occlusion and Motion Reasoning for Long-Term Tracking , 2014, ECCV.

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

[9]  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).

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

[11]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

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

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

[14]  Hanqing Lu,et al.  Collaborative Correlation Tracking , 2015, BMVC.

[15]  Ming-Hsuan Yang,et al.  Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking , 2017, International Journal of Computer Vision.

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

[17]  Deva Ramanan,et al.  Self-Paced Learning for Long-Term Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Federico Pernici,et al.  FaceHugger: The ALIEN Tracker Applied to Faces , 2012, ECCV Workshops.

[19]  Simon Lucey,et al.  Learning Background-Aware Correlation Filters for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

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

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

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