Abstract
Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently. Nevertheless, the top performance trackers always employ complicated optimization methods which constrain their real-time applications. How to accelerate the tracking speed while retaining the tracking accuracy is a significant issue. In this paper, we propose a multi-level CF-based tracking approach named MLCFT which further explores the potential capacity of CF with two-stage detection: primal detection and oriented re-detection. The cascaded detection scheme is simple but competent to prevent model drift and accelerate the speed. An effective fusion method based on relative entropy is introduced to combine the complementary features extracted from deep and shallow layers of convolutional neural networks (CNN). Moreover, a novel online model update strategy is utilized in our tracker, which enhances the tracking performance further. Experimental results demonstrate that our proposed approach outperforms the most state-of-the-art trackers while tracking at speed of exceeded 16 frames per second on challenging benchmarks.
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References
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.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
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 (CVPR), pp. 2544–2550 (2010)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)
Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference (BMVC) (2014)
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: International Conference on Computer Vision Workshops (ICCVW), pp. 58–66 (2015)
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: International Conference on Computer Vision, pp. 4310–4318 (2015)
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: Computer Vision and Pattern Recognition, pp. 1430–1438 (2016)
Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29
Galoogahi, H.K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)
Gao, P., et al.: Adaptive object tracking with complementary models. IEICE Trans. Inf. Syst. E101-D(11), 2849–2854 (2018)
Gao, P., Ma, Y., Song, K., Li, C., Wang, F., Xiao, L.: A complementary tracking model with multiple features. arXiv preprint arXiv:1804.07459 (2018)
Gao, P., Ma, Y., Song, K., Li, C., Wang, F., Xiao, L.: Large margin structured convolution operator for thermal infrared object tracking. In: International Conference on Pattern Recognition (ICPR), pp. 2380–2385 (2018)
Gao, P., et al.: High performance visual tracking with circular and structural operators. Knowl.-Based Syst. 161, 240–253 (2018)
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 (CVPR), pp. 580–587 (2014)
Gundogdu, E., Alatan, A.: Good features to correlate for visual tracking. arXiv preprint arXiv:1704.06326 (2017)
He, Z., Fan, Y., Zhuang, J., Dong, Y., Bai, H.: Correlation filters with weighted convolution responses. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2017)
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
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 (2015)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732 (2014)
Kristan, M., et al.: The visual object tracking VOT2017 challenge results. In: International Conference on Computer Vision (ICCV), pp. 1949–1972 (2016)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_18
Liu, Q., Lu, X., He, Z., Zhang, C., Chen, W.S.: Deep convolutional neural networks for thermal infrared object tracking. Knowl.-Based Syst. 134, 189–198 (2017)
Lukežič, A., Vojíř, T., Čehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4847–4856. IEEE (2017)
Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)
Qi, Y., et al.: Hedged deep tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4303–4311 (2016)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W., Yang, M.H.: CREST: convolutional residual learning for visual tracking. In: International Conference on Computer Vision (ICCV), pp. 2574–2583 (2017)
Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1420–1429 (2016)
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5000–5008 (2017)
Vedaldi, A., Lenc, K.: MatConvnet: convolutional neural networks for MATLAB. In: ACM International Conference on Multimedia (ACMMM), pp. 689–692 (2015)
Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418 (2013)
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)
Zhang, T., Xu, C., Yang, M.H.: Multi-task correlation particle filter for robust object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Acknowledgements
This work is supported by the NSFC project under Grant No. U1833101, Shenzhen Science and Technologies project under Grant No. JCYJ20160428182137473, the Science and Technology Planning Program of Guangdong Province under Grant No. 2016B090918047, and the Joint Research Center of Tencent & Tsinghua.
The authors would like to thank all the anonymous reviewers for their constructive comments and suggestions.
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Ma, Y., Yuan, C., Gao, P., Wang, F. (2019). Efficient Multi-level Correlating for Visual Tracking. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_29
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