Long-term Target Tracking Algorithm Based on Improved Kernel Correlation Filter

doi:10.3969/j.issn.1006-2475.2019.01.002

Abstract

Abstract: Aiming at this problem that the tracking algorithm of kernel correlation filter cannot track the target accurately again after the loss of the visual target because of the occlusion, an improved kernel correlation filter tracking algorithm based on GM(1,1) grey prediction model and interval template matching is proposed. The experimental results show that the proposed algorithm has a great improvement compared with the traditional kernelized correlation filter tracking algorithm in the complex environment. At the same time, it has some advantages over other state-of-the-art methods.

Key words: visual target tracking, kernelized correlation filter, validation region, GM(1,1) grey prediction model

CLC Number:

TP391

ZHANG Xue 1, NI Jian-jun 1,2, CHEN Yan1. Long-term Target Tracking Algorithm Based on Improved Kernel Correlation Filter[J]. Computer and Modernization, 2019, 0(01): 6-.

References

［1］ KANELLAKIS C, NIKOLAKOPOULOS G. Survey on Computer Vision for UAVs: Current Developments and Trends［M］. Kluwer Academic Publishers, 2017.
［2］练海晨,蒋亚平. 面向物流仓库的多目标视频跟踪［J］. 计算机与现代化, 2014(9):77-81.
［3］李鹏飞,雷宏. 基于参数学习和运动预测的在线多目标跟踪算法［J］. 计算机与现代化, 2017(10):81-86.
［4］ SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking: An experimental survey［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014,36(7):1442-1468.
［5］〖KG-*3〗WU Y, LIM J, YANG M H. Online object tracking: A benchmark［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2013:2411-2418.
［6］ YUAN Y, YANG H, FANG Y M, et al. Visual object tracking by structure complexity coefficients［J］. IEEE Transactions on Multimedia, 2015,17(8):1125-1136.
［7］ ADHIKARI G, SAHANI S K, CHAUHAN M S, et al. Fast real time object tracking based on normalized cross correlation and importance of thresholding segmentation［C］// 2016 International Conference on Recent Trends in Information Technology (ICRTIT). 2016.
［8］ BAKER S, MATTHEWS I. Lucas-Kanade 20 years on: A unifying framework［J］. International Journal of Computer Vision, 2004,56(3):221-255.
［9］ BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters［C］// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010:2544-2550.
［10］HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels［C］// Proceedings of the 12th European Conference on Computer Vision. 2012:702-715.
［11］HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015,37(3):583-596.
［12］PAN Z F, ZHU Y L. Kernelized correlation filters object tracking method with multi-scale estimation［J］. Laser & Optoelectronics Progress, 2016(10):193-199.
［13］杨武艳,郁钟铭. GM(1,1)灰色预测模型在矿井瓦斯涌出量预测中的应用［J］. 矿业工程研究, 2012,27(4):46-49.
［14］杨家慧,刘方爱. 基于巴氏系数和Jaccard系数的协同过滤算法［J］. 计算机应用, 2016,36(7):2006-2010.
［15］HONG Z B, CHEN Z, WANG C H, et al. Multi-store tracker(MUSTer): A cognitive psychology inspired approach to object tracking［C］// 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:749-758.
［16］DANELLJAN M, HGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking［C］// Proceedings of British Machine Vision Conference. 2014:65.1-65.11.
［17］LI Y, ZHU J K. A scale adaptive kernel correlation filter tracker with feature integration［C］// 2014 European Conference on Computer Vision. 2014:254-265.
［18］ZHANG K H, ZHANG L, YANG M H. Real-time compressive tracking［C］// 2012 European Conference on Computer Vision. 2012:864-877.
［19］SEVILLA-LARA L, LEARNED-MILLER E. Distribution fields for tracking［C］// 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012:1910-1917.