Abstract
Although Correlation Filters (CF) tracking algorithms have inherent capability to tackle various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking based on Correlation Filters, feature is one of the most important factors due to its representation power of target appearance. In this paper, we proposed a new tracking framework by integrating the advantage of complementary features to achieve robust tracking performance. The key issue of this work lies in the fact that different features respond to different tracking challenges, which also applies to deep learning features and hand-craft features. Moreover, for the tracking speed balance, we train a light-weight deep CNN features by using end-to-end learning method, which has the same Parameter magnitude as the hand-crafted features. Experimental results on OTB-2013, OTB-2015 large benchmarks datasets show that the proposed tracker performs favorably against several state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Junseok, K., Leek, M.L.: Visual tracking decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276. IEEE Press, San Francisco (2010)
Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: International Conference on Computer Vision, pp. 263–270. IEEE Computer Society (2011)
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)
Bolme, D.S., Beveridge, J.R.: Visual object tracking using adaptive correlation filters. Comput. Vis. Pattern Recognit. 119(5), 2544–2550 (2010)
Galoogahi, H. K., Sim, T., Lucey, S.: Multi-channel correlation filters. In: IEEE International Conference on Computer Vision, pp. 3072–3079, IEEE (2014)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50
Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 809–817. Curran Associates Inc., Lake Tahoe (2013)
Zhang, K., Liu, Q., Wu, Y., Yang, M.H.: Robust visual tracking via convolutional networks without training. IEEE Trans. Image Process. 25(4), 1779–1792 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural network. Adv. Neural. Inf. Process. Syst. 60(1), 1097–1105 (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE (2016)
Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision, pp. 3119–3127. IEEE (2015)
Wang, L., Ouyang, W., Wang, X., Lu, H.: STCT: sequentially training convolutional networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1373–1381. IEEE (2016)
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for correlation filter based tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000–5008. IEEE (2017)
Wang, Q., Gao, J., Xing, J., Zhang, M., Hu, W.: Dcfnet: discriminant correlation filters network for visual tracking. arXiv:2017.1704.04057, http://cn.arxiv.org/abs/1704.04057
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.: Staple: complementary learners for real-time tracking. Comput. Vis. Pattern Recognit. 38(2), 1401–1409 (2016)
Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit. 69(1), 82–93 (2017)
Ma, C., Xu, Y., Ni, B., Yang, X.: When correlation filters meet convolutional neural networks for visual tracking. IEEE Signal Process. Lett. 23(10), 1454–1458 (2016)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 119, pp. 2544–2550. IEEE (2010)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)
Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, vol. 65, pp. 1–11 (2014)
Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1387–1395. IEEE (2016)
Danelljan, M., Häger, G., Khan, F. S., Felsberg, M.: Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1430–1438. IEEE (2016)
Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396. IEEE (2015)
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.: Staple: complementary learners for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 38, no. 2, pp. 1401–1409. IEEE (2015)
Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit. 69, 82–93 (2017)
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: IEEE International Conference on Computer Vision, pp. 3074–3082. IEEE Computer Society (2015)
Zhang, L., Varadarajan, J., Suganthan, P.N., Ahuja, N., Moulin, P.: Robust visual tracking using oblique random forests. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5825–5834. IEEE (2017)
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: efficient convolution operators for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931–6939. IEEE (2017)
Bertinetto, L., Valmadre, J., Henriques, João F., Vedaldi, A., Torr, Philip H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 2014.1409.1556. http://cn.arxiv.org/abs/1409.1556
Russakovsky, O., Deng, J., Su, H.: imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)
Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 188–203. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_13
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, W., Li, W., Shi, M. (2018). Correlation Filter Tracking with Complementary Features. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-04224-0_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04223-3
Online ISBN: 978-3-030-04224-0
eBook Packages: Computer ScienceComputer Science (R0)