Abstract
The traditional target tracking algorithm adopts artificial features. However, the artificial features are not strong enough to illustrate the appearance of the target. So it is difficult to apply to complex scenes; moreover, the traditional target tracking algorithm does not judge the confidence of the response. This paper proposes the Multiple Features and Average Peak Correlation Energy (MFAPCE) tracking algorithm, MFAPCE tracking algorithm combines deep features with color features and uses average peak correlation energy to measure confidence. Finally, according to the confidence to determine whether to update the model. Compared with the traditional tracking algorithm, MFAPCE algorithm can improve the tracking performance according to experiment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: International Conference on Neural Information Processing Systems, pp. 809–817. Curran Associates Inc (2013)
Gao, J., Ling, H., Hu, W., et al.: Transfer learning based visual tracking with Gaussian processes regression. In: Computer Vision—ECCV 2014, pp. 188–203. Springer International Publishing (2014)
Valmadre, J., Bertinetto, L., Henriques, J., et al.: End-to-End representation learning for correlation filter based tracking. 5000–5008 (2017)
Henriques J.F., Caseiro R, et al.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Computer Vision—ECCV 2012, pp. 702–715. Springer, Berlin, Heidelberg (2012)
Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps 4800–4808 (2017)
Qi, Y., Zhang, S., Qin, L., et al.: Hedged deep tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311 (2016)
Ma, C., Huang, J.B., Yang, X., et al.: Hierarchical convolutional features for visual tracking. In: IEEE Conference on IEEE International Conference on Computer Vision, pp. 3074–3308 (2016)
Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1387–1395. IEEE Computer Society (2017)
Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Possegger, H., Mauthner, T., Bischof, H.: In defense of color-based model-free tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2113–2120 (2015)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (61561016, 11603041), Guangxi Information Science Experiment Center funded project, Department of Science and Technology of Guangxi Zhuang Autonomous Region (AC16380014, AA17202048, AA17202033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sun, X., Zhang, K., Ji, Y., Wang, S., Yan, S., Wu, S. (2020). Correlation Filter Tracking Algorithm Based on Multiple Features and Average Peak Correlation Energy. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-04946-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04945-4
Online ISBN: 978-3-030-04946-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)