Correlation Filter Tracking Based on Online Detection and Scale-Adaption

WANG Yanchuan1 HUANG Hai1 LI Shaomei1 GAO Chao1

(1.National Digital Switching System Engineering Technological Research Center, Zhengzhou, Henan, China 450000)

【Abstract】In correlation filter tracking, occlusion and object scale change can cause tracking failure easily. To deal with this problem, a correlation filter tracking algorithm based on online detection and scale-adaption is proposed. The target is initially located through a correlation filter tracker melting with histogram features of oriented gradient, color attribute features and illumination invariant features. The reconstruction residual of local sparse representation model is used for occlusion discrimination. If occlusion occurs, online support vector machine detection will be carried out and target relocating will be realized. Scale estimation from coarse to precise is carried out, and precise scale of target is obtained by scale pre-estimation and Newton iterative method. A balanced model updating strategy is used to update correlation filter regularly and update sparse representation model and support vector machine conservatively. Experimental results show that, compared with existing tracking algorithms, the proposed algorithm can effectively reduce the occlusion, target scale change and other complicated factors, which can gain higher distance precision and success rate on 50 groups of test sequences. The overall performance of the proposed algorithm is better than other existing algorithms.

【Keywords】 machine vision; object tracking; correlation filtering; online detection; scale estimation; model update;

【DOI】

【Funds】 National Natural Science Foundation of China (61601513, 61379151) Excellent Youth Foundation of He’nan Scientific Committee (144100510001)

Download this article

(Translated by ZHANG XY)

    References

    [1] Chen Z, Hong Z B, Tao D C. An experimental survey on correlation filter-based tracking [OL]. Computer Science, 2015, 53 (6025): 68–83.

    [2] Zhao G P, Shen Y P, Wang J Y. Adaptive feature fusion object tracking based on circulant structure with kernel [J]. Acta Optica Sinica, 2017, 37 (8): 0815001 (in Chinese).

    [3] Ma C, Xu Y, Ni B B, et al. When correlation filters meet convolutional neural networks for visual tracking [J]. IEEE Signal Processing Letters, 2016, 23 (10): 1454–1458.

    [4] Bolme D S, Beveridge J R, Draper B A, et al. Visual object tracking using adaptive correlation filters [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010: 2544–2550.

    [5] Henriques J F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels [C]∥Proceedings of European Conference on Computer Vision, 2012: 702–715.

    [6] Henriques J F, Caseiro R, Martins P, et al. Highspeed tracking with kernelized correlation filters [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37 (3): 583–596.

    [7] Danelljan M, Khan F S, Felsberg M, et al. Adaptive color attributes for real-time visual tracking [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1090–1097.

    [8] Qi Y, Zhang S, Qin L, et al. Hedged deep tracking [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4303–4311.

    [9] Danelljan M, Hager G, Khan F S, et al. Accurate scale estimation for robust visual tracking [C]∥Proceedings of British Machine Vision Conference, 2014: 1–11.

    [10] Li Y, Zhu J K. A scale adaptive kernel correlation filter tracker with feature integration [C]∥Proceedings of European Conference on Computer Vision, 2014: 254–265.

    [11] Ma C, Yang X K, Zhang C Y, et al. Long-term correlation tracking [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5388–5396.

    [12] Zhang J, Ma S, Sclaroff S. MEEM: Robust tracking via multiple experts using entropy minimization [C]∥Proceedings of European Conference on Computer Vision, 2014: 188–203.

    [13] Zhong W, Lu H C, Yang M H. Robust object tracking via sparse collaborative appearance model [J]. IEEETransactions on Image Processing, 2014, 23 (5): 2356–2368.

    [14] Wang Z, Vucetic S. Online training on a budget of support vector machines using twin prototypes [J]. Statistical Analysis and Data Mining, 2010, 3 (3): 149–169.

    [15] Scholkopf B, Smola A J. Learning with kernels: Support vector machines, regularization, optimization, and beyond [M]. London: MIT Press, 2002: 405–423.

    [16] Wu Y, Lim J, Yang M H. Online object tracking: Abenchmark [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2411–2418.

    [17] Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1822–1829.

    [18] Hare S, Saffari A, Torr P H S. Struck: Structured output tracking with kernels [C]∥Proceedings of IEEE International Conference on Computer Vision, 2011: 263–270.

    [19] Kalal Z, Mikolajczyk K, Matas J. Tracking learning detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34 (7): 1409–1422.

    [20] Bao C L, Wu Y, Ling H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1830–1837.

    [21] Luo H L, Du F F, Kong F S. Pixel feature-weighted scale-adaptive object tracking algorithm [J]. Journal on Communications, 2015, 36 (10): 200–211 (in Chinese).

    [22] Bertinetto L, Valmadre J, Golodetz S, et al. Staple: Complementary learners for real-time tracking [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1401–1409.

    [23] Ning J F, Yang J M, Jiang S J, et al. Object tracking via dual linear structured SVM and explicit feature map [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4266–4274.

This Article

ISSN:0253-2239

CN:31-1252/O4

Vol 38, No. 02, Pages 227-239

February 2018

Downloads:0

Share
Article Outline

Abstract

  • 1 Introduction
  • 2 Algorithm description
  • 3 Experiment result and discussion
  • 4 Conclusion
  • References