Global Image Correlation Filter with H-D Fusion Mechanism for Visual Tracking
Abstract
The discriminative correlation filter has remarkable robustness under various circumstances in the visual tracking of videos. Nevertheless, there are some challenges for the further improvement of the tracking performance, including confusion between the tracked target, complex background, and underfitting because of a lack of training samples. The main contribution of the study is developing a new framework which combines with global image sampling enable to significantly augment the negative samples without the need of corruptting those positive samples. Besides we propose a fusion mechanism by combining two image patch representations in accordance with their confidence scores. Obtained results show that the proposed method is an effective scheme in leveraging the complementary properties of deep and hand-crafted features. Based on the conducted experiments on several benchmarks such as OTB2013, TB-50, and TB-100 datasets, it is concluded that the proposed approaches can achieve better result compared to the state-ofthe-art methods.
References
[1]
Mohammad A. Alsmirat, Fatimah Al-Alem, Mahmoud Al-Ayyoub, Yaser Jararweh, and Brij Gupta. 2018. Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimedia Tools & Applications 4 (2018), 1--40.
[2]
Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, and Philip H. S. Torr. 2016. Staple: Complementary Learners for Real-Time Tracking. In Computer Vision & Pattern Recognition.
[3]
Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. 2016. Fully-convolutional sia mese networks for object tracking. In European conference on computer vision. Springer, 850--865.
[4]
David S. Bolme, J. Ross Beveridge, Bruce A. Draper, and Yui Man Lui. 2010. Visual object tracking using adaptive correlation filters. In Computer Vision & Pattern Recognition.
[5]
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. 2010. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations & Trends in Machine Learning 3, 1 (2010), 1--122.
[6]
Navneet Dalal and Bill Triggs. 2005. Histograms of Oriented Gradients for Human Detection. In IEEE Computer Society Conference on Computer Vision & Pattern Recognition.
[7]
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg. 2017. ECO: Efficient convolution operators for tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition. 6638--6646.
[8]
Martin Danelljan, Gustav Häger, Fahad Khan, and Michael Felsberg. 2014. Accurate scale estimation for robust visual tracking. In British Machine Vision Conference, Nottingham, September 1-5, 2014. BMVA Press.
[9]
Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2015. Learning Spatially Regularized Correlation Filters for Visual Tracking. In 2015 IEEE International Conference on Computer Vision (ICCV).
[10]
Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2016. Convolutional Features for Correlation Filter Based Visual Tracking. In IEEE International Conference on Computer Vision Workshop.
[11]
Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2016. Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1430--1438.
[12]
Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg, and Joost Van De Weijer. 2014. Adaptive Color Attributes for Real-Time Visual Tracking. In IEEE Conference on Computer Vision & Pattern Recognition.
[13]
Jia Deng, Wei Dong, Richard Socher, Li Jia Li, Kai Li, and Fei Fei Li. 2009. ImageNet: a Large-Scale Hierarchical Image Database. In IEEE Conference on Computer Vision & Pattern Recognition.
[14]
Hamed Kiani Galoogahi, Terence Sim, and Simon Lucey. 2015. Correlation Filters with Limited Boundaries. In Computer Vision & Pattern Recognition.
[15]
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In IEEE Conference on Computer Vision & Pattern Recognition.
[16]
J. F. Henriques, R Caseiro, P Martins, and J Batista. 2015. High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis & Machine Intelligence 37, 3 (2015), 583--596.
[17]
Hamed Kiani Galoogahi, Ashton Fagg, and Simon Lucey. 2017. Learning background-aware correlation filters for visual tracking. In Proceedings of the IEEE international conference on computer vision. 1135--1143.
[18]
Matej Kristan, Jiri Matas, Ales Leonardis, Tomas Vojir, Roman Pflugfelder, Gustavo Fernandez, Georg Nebehay, Fatih Porikli, and Luka Cehovin. 2016. A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis & Machine Intelligence 38, 11 (2016), 2137--2155.
[19]
Jin Li, Lichao Sun, Qiben Yan, Zhiqiang Li, Witawas Srisa-an, and Heng Ye. 2018. Significant permission identification for machine-learning-based android malware detection. IEEE Transactions on Industrial Informatics 14, 7 (2018), 3216--3225.
[20]
Ya Li, Guangrun Wang, Lin Nie, Qing Wang, and Wenwei Tan. 2018. Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recognition 75 (2018), 51--62.
[21]
Zhiyong Li, Gao Song, and Nai Ke. 2017. Robust Object Tracking Based on Adaptive Templates Matching via the Fusion of Multiple Features. Journal of Visual Communication & Image Representation 44 (2017), 1--20.
[22]
Chao Ma, Xiaokang Yang, Chongyang Zhang, and Ming-Hsuan Yang. 2015. Long-term correlation tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5388--5396.
[23]
Jifeng Ning, Jimei Yang, Shaojie Jiang, Zhang Lei, and Ming Hsuan Yang. 2016. Object Tracking via Dual Linear Structured SVM and Explicit Feature Map. In Computer Vision & Pattern Recognition.
[24]
Shaul Oron, Aharon Bar-Hillel, Levi Dan, and Shai Avidan. 2015. Locally Orderless Tracking. International Journal of Computer Vision 111, 2 (2015), 213--228.
[25]
Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, and Ming-Hsuan Yang. 2016. Hedged Deep Tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4303--4311.
[26]
Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science (2014).
[27]
Arnold WM Smeulders, Dung M Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan, and Mubarak Shah. 2013. Visual tracking: An experimental survey. IEEE transactions on pattern analysis and machine intelligence 36, 7 (2013), 1442--1468.
[28]
Yi Wu, Jongwoo Lim, and Ming Hsuan Yang. 2015. Object Tracking Benchmark. IEEE Transactions on Pattern Analysis & Machine Intelligence 37, 9 (2015), 1834--1848.
[29]
Jia Xu, Huchuan Lu, and Ming Hsuan Yang. 2012. Visual tracking via adaptive structural local sparse appearance model. In IEEE Conference on Computer Vision & Pattern Recognition.
[30]
Wu Yi, Jongwoo Lim, and Ming Hsuan Yang. 2013. Online Object Tracking: A Benchmark. In Computer Vision & Pattern Recognition.
[31]
Yang Yi, Liping Luo, and Zhenxian Zheng. 2018. Single online visual object tracking with enhanced tracking and detection learning. Multimedia Tools and Applications 2 (2018).
[32]
Yutong Yu and Qing Zhu. 2016. The method of multi-step dimensionality reduction and parallel feature fusion in clothing recognition. In International Conference on Artificial Intelligence & Robotics & the International Conference on Automation.
[33]
Baochang Zhang, Shangzhen Luan, Chen Chen, Jungong Han, Wang Wei, Alessandro Perina, and Shao Ling. 2017. Latent Constrained Correlation Filter. IEEE Transactions on Image Processing PP, 99 (2017), 1--1.
Index Terms
- Global Image Correlation Filter with H-D Fusion Mechanism for Visual Tracking
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Published In
October 2020
552 pages
ISBN:9781450387835
DOI:10.1145/3436369
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In-Cooperation
- Beijing University of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2021
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- Research-article
- Research
- Refereed limited
Funding Sources
- Guangzhou Science and Technology Project with No.202002030273, National Natural Science Foundation of China
Conference
ICCPR 2020
ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition
October 30 - November 1, 2020
Xiamen, China
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