A new TLD target tracking method based on improved correlation filter and adaptive scale

Target tracking is a popular but challenging problem in computer vision field. Due to many disturbing factors such as position transformation, illumination, and occlusion, it is difficult to achieve continuous target tracking. On the basis of the above analyses, a novel target tracking method based on correlation filters is proposed in this paper. This method uses the improved Tracking–Learning–Detection (TLD) tracking framework which combines the tracker with the detector through the learning mechanism. In the TLD tracking framework, the Spatially Regularized Discriminatively Correlation Filters tracker is used and improved. In addition, the adaptive tracking scale is realized according to the confidence of the searching area. The experimental results show that the proposed algorithm can effectively deal with the attitude change and the illumination problem so that it has better robustness and stability for target continuous tracking.

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

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

[3]  Fazhi He,et al.  Part-based visual tracking with spatially regularized correlation filters , 2019, The Visual Computer.

[4]  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).

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

[6]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

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

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

[9]  Sridha Sridharan,et al.  An adaptive optical flow technique for person tracking systems , 2007, Pattern Recognit. Lett..

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

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

[12]  Sidi Ahmed Mahmoudi,et al.  Real-time motion tracking using optical flow on multiple GPUs , 2014 .

[13]  Guixi Liu,et al.  Coupled-layer based visual tracking via adaptive kernelized correlation filters , 2016, The Visual Computer.

[14]  Hailong Pei,et al.  Meanshift algorithm based on kernel bandwidth adaptive adjust , 2013, Proceedings of the 32nd Chinese Control Conference.

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

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

[17]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognit. Lett..

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

[19]  Jiri Matas,et al.  Online learning of robust object detectors during unstable tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[20]  Changsheng Xu,et al.  Correlation Particle Filter for Visual Tracking , 2018, IEEE Transactions on Image Processing.

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

[22]  Jiri Matas,et al.  Face-TLD: Tracking-Learning-Detection applied to faces , 2010, 2010 IEEE International Conference on Image Processing.

[23]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  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).

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

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

[27]  Lin Wang,et al.  Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture , 2011 .

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

[29]  Jun Gao,et al.  Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection , 2014, IEEE Transactions on Cybernetics.