Abstract
Object tracking has been widely used in artificial intelligence, military reconnaissance, security monitoring and other fields. It has become a research hotspot of computer vision. To handle the drift problem in the presence of occlusions, a tracker combined with spatio-temporal context information and correlation filter is proposed in this paper. HOG (Histogram of Oriented Gradient), CN (Color Name) and gray features are extracted to learn the correlation filter. Meanwhile, the spatio-temporal context model is trained. The response map of correlation filter and spatio-temporal context model are normalized and fused. Experimental results show that the proposed algorithm can accurately track the object, and has better performance in terms of successful rate, center position error and distance precision.
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Yilmaz, A., Javad, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 1–45 (2006)
Kim, D.Y., Jeon, M.: Spatial-temporal auxiliary particle filtering with l1-norm based appearance model learning for robust visual tracking. IEEE Trans. Image Process. 22(2), 511–522 (2013)
Zhang, K.H., Zhang, L., Liu, Q.S., et al.: Fast visual tracking via dense spatio-temporal context learning. In: ECCV, Zurich, pp, 127–141. Springer, Switzerland (2014)
Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: CVPR, King Abdullah University of Science and Technology, pp, 1396–1404 (2017)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2011)
Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR, pp, 1177–1184 (2011)
Xu, J.Q., Lu, Y., Liu, J.W.: Robust tracking via weighted spatio-temporal context learning. In: Proceedings of the IEEE International Conference on Image Processing, pp. 413–416 (2014)
Liu, W.J., Dong, S.H., Qu, H.C.: Anti-occlusion visual tracking algorithm based on spatio-temporal context learning. J. Image Graph. 21(8), 1057–1067 (2016)
Yu, L.Y., Fan, C.X., Ming, Y.: Improved object tracking algorithm based on kernelized correlation filter. J. Comput. Appl. 35(12), 3550–3554 (2015)
Qian, T.H., Luo, Z.Q., Li, G.J., et al.: Scale adaptive improvement of kernel correlation filter tracking algorithm. J. Comput. Appl. 37(3), 811–816 (2017)
Lu, J.H., Chen, Y.M., Zou, Y.B., Zou, G.Z.: Long-term tracking based on spatio-temporal context. J. Shanghai Jiao Tong Univ. 22(4), 504–512 (2017)
David, S.B.J., Ross, B., Bruce, A.: Visual object tracking using adaptive correlation filters. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)
Joao, F.H., Rui, C., Pedro, M., Jorge, B.: Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV, pp. 702–715 (2012)
Henriques, J.F., Caseiro, R., Martins, P.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV, pp. 254–265 (2014)
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC (2014)
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV, pp. 4310–4318 (2015)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark, pp. 2411–2418. IEEE Computer Society, United States (2013)
Zheng, Y., Chen, Q.Q., Zhang, Y.J.: Deep learning and its new progress in object and behavior recognition. J. Image Graph. 19(2), 175–184 (2014)
Acknowledgments
This work is supported by the graduate innovation fund project of Xi’an university of posts and telecommunications. (Grant No. CXJJLY2018027). Shaanxi Science and Technology Innovation and Entrepreneurship Dual Tutor System Project (2019JM-604), National Science Foundation of China (61601362, 61571361).
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Die, J., Li, N., Liu, Y., Wu, Y. (2020). Correlation Filter Tracking Algorithm Based on Spatio-Temporal Context. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_30
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DOI: https://doi.org/10.1007/978-3-030-32456-8_30
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