Filter Tracking Based on Time Regularization and Background-Aware

Mingming Liu; Dong Pei; Jü Liu; Donghui Zhu; Haoxiang Sun

doi:10.3788/LOP56.231503

[1] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1834-1848(2015).

[2] Danelljan M, Häger G, Khan F S et al. Learning spatially regularized correlation filters for visual tracking. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 4310-4318(2015).

[3] Dong Q J, He X D, Ge H Y et al. Adaptive merging complementary learners for visual tracking based on probabilistic model[J]. Laser & Optoelectronics Progress, 56, 161505(2019).

[4] He X D, Zhou S Z. Fast scale adaptive kernel correlation filtering algorithm for target tracking[J]. Laser & Optoelectronics Progress, 55, 121501(2018).

[5] Bolme D, 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, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2544-2550(2010).

[6] Zhang Y Y. Object tracking based on similar background and color histogram in HSV color space[J]. Electronics Optics & Control, 26, 100-105(2019).

[7] Ma C, Huang J B, Yang X K et al. Hierarchical convolutional features for visual tracking. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 3074-3082(2015).

[8] Danelljan M, Robinson A, Khan F S et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9909, 472-488(2016).

[9] Dalal N, Triggs B. Histograms of oriented gradients for human detection. [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 8588935(2005).

[10] Danelljan M, Hager G, Khan F S et al. Convolutional features for correlation filter based visual tracking. [C]∥2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile. New York: IEEE, 621-629(2015).

[11] Bertinetto L, Valmadre J, Henriques J F et al. Fully-convolutional Siamese networks for object tracking[M]. ∥Hua G, Jégou H. Computer vision-ECCV 2016 Workshops. Lecture notes in computer science. Cham: Springer, 9914, 850-865(2016).

[12] Possegger H, Mauthner T, Bischof H. In defense of color-based model-free tracking. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2113-2120(2015).

[13] Mao N, Yang D D, Li Y et al. Spatial regularization correlation filtering tracking via deformable diversity similarity[J]. Acta Optica Sinica, 39, 0415002(2019).

[14] Yang D D, Mao N, Yang F C et al. Improved SRDCF object tracking via the best-buddies similarity[J]. Optics and Precision Engineering, 26, 492-502(2018).

[15] Li F, Tian C, Zuo W M et al. Learning spatial-temporal regularized correlation filters for visual tracking. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4904-4913(2018).

[16] Galoogahi H K, Sim T, Lucey S. Multi-channel correlation filters. [C]∥2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia. New York: IEEE, 3072-3079(2013).

[17] Zhang H, Du Y, Ning S R et al. Pedestrian detection method based on Faster RCNN[J]. Sensors and Microsystems, 38, 147-149, 153(2019).

[18] Boyd S, Parikh N, Chu E et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends ^®in Machine Learning, 3, 1-122(2010). http://dl.acm.org/citation.cfm?id=2185815.2185816&coll=DL&dl=GUIDE&CFID=358389691&CFTOKEN=89770224

[19] Sherman J, Morrison W J. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix[J]. The Annals of Mathematical Statistics, 21, 124-127(1950). http://www.jstor.org/stable/2236561

[20] Henriques J F, Caseiro R, Martins P et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583-596(2015).

[21] Bertinetto L, Valmadre J, Golodetz S et al. Staple: complementary learners for real-time tracking. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1401-1409(2016).

[22] Danelljan M, Häger G, Khan F S et al[M]. Accurate scale estimation for robust visual tracking(2014).

[23] Galoogahi H K, Fagg A, Lucey S. Learning background-aware correlation filters for visual tracking. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 1144-1152(2017).