Abstract：Focusing on the issue that the Kernelized Correlation Filter (KCF) can not respond to the target scale change, a KCF target tracking algorithm based on fast discriminative scale estimation was proposed. Firstly, the target position was estimated by KCF. Then, a fast discriminative scale filter was learned online by using a set of target samples with different scales. Finally, an accurate estimation of the target size was obtained by applying the learned scale filter at the target position. The experiments were conducted on Visual Tracker Benchmark video sequence sets, and comparison was performed with the KCF algorithm based on Discriminative Scale Space Tracking (DSST) and the traditional KCF algorithm. Experimental results show that the tracking accuracy of the proposed algorithm is 2.2% to 10.8% higher than that of two contrast algorithms when the target scale changes, and the average frame rate of the proposed algorithm is also 19.1% to 68.5% higher than that of KCF algorithm based on DSST. The proposed algorithm has strong adaptability and high real-time performance to target scale change.
YILMAZ A, JAVED O, SHAH M. Object tracking:a survey[J]. ACM Journal of Computing Surveys, 2006, 38(4):Article No. 13.
[2]
SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking:an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7):1442-1468.
[3]
ZHAI Y, YEARY M, ZHUOU D. Target tracking using a particle filter based on the projection method[C]//Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2007, 3:1189-1192.
[4]
COMANICIU D, REMESH V, MEER P. Real-time tracking of non-rigid objects using Mean Shift[C]//Proceedings of 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2000, 2:142-149.
[5]
ALLEN J G, XU R Y D, JIN J S. Object tracking using CamShift algorithm and multiple quantized feature spaces[C]//Proceedings of the 2005 Pan-Sydney Area Workshop on Visual Information Processing. New York:ACM, 2005:3-7.
[6]
BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2010:2544-2550.
[7]
HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 2012 European Conference on Computer Vision, LNCS 7575. Berlin:Springer, 2012:702-715.
[8]
HENRIQUES J F, RUI C, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596.
[9]
DANELIJAN M, HÄGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[EB/OL].[2016-05-20]. http:www.bmva.org/bmvc/2014/files/abstract038.pdf.
[10]
LI Y, ZHU J. A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of 2014 European Conference on Computer Vision, LNCS 8926. Cham:Springer, 2014:254-265.
[11]
钱堂慧,罗志清,李果家,等.核相关滤波跟踪算法的尺度自适应改进[J],计算机应用,2017,37(3):811-816.(QIAN T H, LUO Z Q, LI G J, et al. Scale adaptive improvement of kernel correlation filter tracking algorithm[J]. Journal of Computer Applications, 2017, 37(3):811-816.)
[12]
DANELIJAN M, HÄGER G, KHAN F S, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8):1561-1575.
[13]
朱明敏,胡茂海.基于相关滤波器的长时视觉目标跟踪方法[J].计算机应用,2017,37(5):1466-1470.(ZHU M M, HU M H. Long-term visual object tracking algorithm based on correlation filter[J]. Journal of Computer Applications, 2017, 37(5):1466-1470.)
[14]
WU Y, LIM J, YANG M. Online object tracking:a benchmark[C]//Proceedings of the 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:2411-2418.
[15]
RAFAEL C G, RICHARD E W. 数字图像处理[M]. 阮秋琦,阮宇智,译.3版.北京:电子工业出版社,2011:454-455. (RAFAEL C G, RICHARD E W. Digital Image Processing[M]. RUAN Q Q, RUAN Y Z, translated. 3rd ed. Beijing:Publishing House of Electronics Industry, 2011:454-455.)
2019, Vol. 39 
