Multi-channel scale adaptive target tracking based on double correlation filter

被引：0

作者：

Han X. ^{[1
]}

Wang Y. ^{[1
]}

Xie Y. ^{[1
]}

Gao Y. ^{[2
]}

Lu Z. ^{[2
]}

机构：

[1] School of Information Engineering, Shenyang University, Shenyang

[2] College of Information Science and Engineering, Northeastern University, Shenyang

来源：

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 11期

关键词：

Correlation filtering; Feature fusion; Multi-channel; Scale transformation; Target tracking;

D O I：

10.19650/j.cnki.cjsi.J1905219

中图分类号：

学科分类号：

摘要：

Aiming at the instability problem of target tracking caused by scale change and severe apparent change in target tracking, in this paper a multi-channel feature fused scale estimation strategy is designed, and a multi-channel scale adaptive target tracking algorithm based on double correlation filter is proposed. Considering that the features of CN are insensitive to attitude and scale, and the features of HOG have good stability to illumination change and target movement, the features of CN, HOG and gray scale are fused to improve the tracking robustness to target apparent change. On the premise of ensuring minimum error risk, the ridge regression is used to solve the filter. The scale filter is established to realize the multi-scale judgment of the target, so that when the target scale changes the tracking of the target remains stable. The TB-100 data set was used to test the performance of the algorithm in multiple scenarios. The experiment results show that the algorithm has good tracking effect under the conditions of target apparent change, scale transformation and background interference. © 2019, Science Press. All right reserved.

引用

页码：73 / 81

页数：8

共 27 条

[1]

Yuan J., Li Y., Dong X.L., Et al., Target tracking with a mobile robot based on on-line classification for motion patterns, Chinese Journal of Scientific Instrument, 38, 3, pp. 568-577, (2017)

[2]

Jiang N., Visual target tracking algorithm based on dynamic adaptive filtering, Journal of Electronic Measurement and Instrumentation, 33, 2, pp. 140-147, (2019)

[3]

Sun Y.J., Yu J.Y., Wang X.M., Multiple object tracking algorithm for the complex scenario, Chinese Journal of Scientific Instrument, 40, 3, pp. 126-137, (2019)

[4]

Xiong D., Lu H.M., Xiao J.H., Et al., Robust long-term object tracking with adaptive scale and rotation estimation, Acta Automatica Sinica, 45, 2, pp. 289-304, (2019)

[5]

Wei Z.Y., Yang X.W., Face tracking method based on model adaptive threshold segmentation, Journal of Electronic Measurement and Instrumentation, 32, 11, pp. 172-177, (2018)

[6]

Wang J.Z., Lu C.H., Jiang W.W., New multi-target tracking algorithm based on conditional random field, Journal of Electronic Measurement and Instrumentation, 31, 6, pp. 909-913, (2017)

[7]

Babenko B., Yang M.H., Belongie S., Visual tracking with online Multiple Instance Learning, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990, (2009)

[8]

Kalal Z., Mikolajczyk K., Matas J., Tracking-Learning-Detection, IEEE Trans. Pattern Anal. Mach Intell, 34, 7, pp. 1409-1422, (2012)

[9]

Bolme D.S., Beveridge J.R., Draper B.A., Et al., Visual object tracking using adaptive correlation filters, 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13-18, (2010)

[10]

Henriques J.F., Caseiro R., Martins P., Et al., Exploiting the circulant structure of tracking-by-detection with kernels, European Conference on Computer Vision (ECCV 2012), pp. 702-715, (2012)

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