Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking

In view of the problem that the conventional tracker does not adapt to abrupt motion, we propose a tracking algorithm based on the hybrid extended ant lion optimizer with sine cosine algorithm (EALO-SCA) in this paper. Firstly, the multiple elites is used to replace the single elite in the standard ant lion optimizer (ALO). The extended ALO (EALO) can enhance the global exploration ability, which can handle abrupt motion. Secondly, considering that sine cosine algorithm (SCA) has strong local exploitation operator, a hybrid EALO-SCA tracker is proposed using the advantages of both EALO and SCA. The proposed approach can improve tracking accuracy and efficiency. Finally, extensive experimental results in both quantitative and qualitative measures prove that the proposed algorithm is very competitive compared to 7 state-of-the-art trackers, especially for abrupt motion tracking.

[1]  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).

[2]  Aouatif Amine,et al.  A hybrid mobile object tracker based on the modified Cuckoo Search algorithm and the Kalman Filter , 2014, Pattern Recognit..

[3]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Rajesh Kumar,et al.  A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem , 2018 .

[5]  Dongyong Yang,et al.  Fast Moving Object Tracking Algorithm based on Hybrid Quantum PSO , 2013 .

[6]  Shiqiang Hu,et al.  SIFT flow for large-displacement object tracking. , 2014, Applied optics.

[7]  Bir Bhanu,et al.  Real-Time Pedestrian Tracking with Bacterial Foraging Optimization , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[8]  Silvio Savarese,et al.  Learning to Track at 100 FPS with Deep Regression Networks , 2016, ECCV.

[9]  Chao Li,et al.  An Experimental Comparison of Swarm Optimization Based Abrupt Motion Tracking Methods , 2018, IEEE Access.

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

[11]  IEEE conference on computer vision and pattern recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Xiaohai He,et al.  Object tracking using firefly algorithm , 2013, IET Comput. Vis..

[13]  Mohamed A. Tawhid,et al.  A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function , 2017, Memetic Computing.

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

[15]  Huanlong Zhang,et al.  SIFT flow for abrupt motion tracking via adaptive samples selection with sparse representation , 2017, Neurocomputing.

[16]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[17]  Weihua Gui,et al.  A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification , 2013, J. Appl. Math..

[18]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[19]  Lei Zhang,et al.  Fast Compressive Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  Harish Bhaskar,et al.  Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Huchuan Lu,et al.  Robust Visual Tracking via Least Soft-Threshold Squares , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

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

[24]  Haibin Ling,et al.  Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Hany M. Hasanien,et al.  Optimal power flow solution in power systems using a novel Sine-Cosine algorithm , 2018, International Journal of Electrical Power & Energy Systems.

[26]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[27]  Pardeep Sharma,et al.  Optimization of photovoltaic power system: a comparative study , 2017 .

[28]  Rynson W. H. Lau,et al.  CREST: Convolutional Residual Learning for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[30]  Jie Yang,et al.  Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning , 2018, IEEE Transactions on Cybernetics.

[31]  Xuejie Zhang,et al.  Video object tracing based on particle filter with ant colony optimization , 2010, 2010 2nd International Conference on Advanced Computer Control.

[32]  Ravi Kumar Jatoth,et al.  Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking , 2018, Appl. Soft Comput..

[33]  Tong Zhou,et al.  Extended kernel correlation filter for abrupt motion tracking , 2017, KSII Trans. Internet Inf. Syst..

[34]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

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

[36]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

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

[38]  Evon M. O. Abu-Taieh,et al.  Comparative Study , 2020, Definitions.

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

[40]  Swagatam Das,et al.  A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking , 2018, Swarm Evol. Comput..

[41]  Matteo Matteucci,et al.  A revaluation of frame difference in fast and robust motion detection , 2006, VSSN '06.

[42]  M. H. Khan,et al.  A Multiple Motion Model Tracker Handling Occlusion and Rapid Motion Variation , 2013 .

[43]  M. Hariharan,et al.  Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism , 2017, Neural Comput. Appl..

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

[45]  Aboul Ella Hassanien,et al.  Binary ant lion approaches for feature selection , 2016, Neurocomputing.

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

[47]  Luc Van Gool,et al.  European conference on computer vision (ECCV) , 2006, eccv 2006.

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

[49]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Changsheng Xu,et al.  Robust Visual Tracking via Exclusive Context Modeling , 2016, IEEE Transactions on Cybernetics.

[51]  Gaige Wang,et al.  A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization , 2013, J. Appl. Math..

[52]  Soheyl Khalilpourazari,et al.  SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process , 2018 .

[53]  Xiaoqin Zhang,et al.  Sequential particle swarm optimization for visual tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  Yong Wang,et al.  Extended cuckoo search-based kernel correlation filter for abrupt motion tracking , 2018, IET Comput. Vis..

[55]  Amir Hossein Gandomi,et al.  Hybrid krill herd algorithm with differential evolution for global numerical optimization , 2014, Neural Computing and Applications.

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

[57]  Hossam M. Zawbaa,et al.  Feature selection based on antlion optimization algorithm , 2015, 2015 Third World Conference on Complex Systems (WCCS).

[58]  Honglun Wang,et al.  Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle , 2017, Soft Comput..