Efficient correlation filter tracking with adaptive training sample update scheme

Visual tracking serves as a significant module in many applications. However, the heavy computation and low speed of many recent trackers restrict their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter based trackers limits their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter based trackers and propose an efficient and adaptive training sample update scheme. Training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with difference hashing algorithm(DHA) or discarded according to tracking result reliability. Experiments on OTB-2015, Temple Color 128 and UAV123 demonstrate our tracker performs favourably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2GHz).

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

[2]  Jiri Matas,et al.  Discriminative Correlation Filter with Channel and Spatial Reliability , 2017, CVPR.

[3]  Yiming Li,et al.  Augmented Memory for Correlation Filters in Real-Time UAV Tracking , 2019, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

[5]  Peng Lu,et al.  Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[6]  Dit-Yan Yeung,et al.  Understanding and Diagnosing Visual Tracking Systems , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Shan Jiang,et al.  Robust Visual Tracking with Channel Weighted Color Ratio Feature , 2019, 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC).

[8]  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).

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

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

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

[12]  Michael Felsberg,et al.  ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[15]  Michael Felsberg,et al.  Deep motion features for visual tracking , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

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

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

[18]  Ramarathnam Venkatesan,et al.  Robust perceptual image hashing via matrix invariants , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

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

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

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

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

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

[24]  Ning Wang,et al.  Reliable Re-Detection for Long-Term Tracking , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Feng Li,et al.  Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

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

[28]  Huchuan Lu,et al.  Visual Tracking via Adaptive Spatially-Regularized Correlation Filters , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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