Abstract
Deep convolutional neural networks (CNNs) have been proved effective in color video visual tracking task. Compared with color video, hyperspectral video contains abundant spectral and material-based information which increases the instance-level discrimination ability. Therefore, hyperspectral video has huge potential for improving the performance of visual tracking task. However, deep trackers based on color video need a large number of samples to train a robust model, while it is difficult to train a hyperspectral video-based CNN model because of the lack of training samples. To tackle with this problem, a novel method is designed on basic of transfer learning technique. At first, a mapping convolutional operation is designed to embed high dimensional hyperspectral video into three channels as color video. Then, the parameters of CNN model learned on color domain are transferred into hyperspectral domain through fine-tuning. Finally, the fine-tuned CNN model is used for hyperspectral video tracking task. The hyperspectral tracker is evaluated on hyperspectral video dataset and it outperforms many state-of-the-art trackers.
Y. Tang—Methodology, Validation, Data duration, Writing-review, Writing-original draft
Y. Liu—Supervision, Investigation, Methodology, Validation, Formal analysis, Writing-review & editing
H. Huang—Methodology, Formal analysis, Validation, Writing-review
C. Zhang—Methodology, Formal analysis, Validation, Writing-review
Y. Li—Validation, Writing-review
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, P.X., Wang, D., et al.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323–338 (2018)
Tang, Y.M., Liu, Y.F., Huang, H., et al.: A scale-adaptive particle filter tracking algorithm based on offline trained multi-domain deep network. IEEE Access 8, 31970–31982 (2020)
Tan, Y.H., Luo, H.Q., Wang, X.P., Liu, M.: Convolutional neural network cascade based neuron termination detection in 3d image stacks. In: 2018 IEEE International Conference on Image Processing, pp. 4048–4052. IEEE, Athens (2018)
Li, Z.Y., Huang, H., Duan, Y.L., Shi, G.Y.: DLPNet: a deep manifold network for feature extraction of hyperspectral imagery. Neural Netw. 129, 7–18 (2020)
Xu, K.J., Huang, H., Deng, P.F., Li, Y.: Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing. IEEE Trans. Neural Netw. Learn. Syst. (2021). https://doi.org/10.1109/TNNLS.2021.3071369
Xu, K.J., Huang, H., Deng, P.F.: Remote sensing image scene classification based on globalClocal dual-branch structure model. IEEE Geosci. Remote Sens. Lett. (2021). https://doi.org/10.1109/LGRS.2021.3075712
Uzair, M., Mahmood, A., Mian, A.: Hyperspectral face recognition with spatiospectral information fusion and PLS regression. IEEE Trans. Image Process. 24, 1127–1137 (2015)
Pu, C.Y., Huang, H., Luo, L.Y.: Classification of hyperspectral image with attention mechanism-based dual-path convolutional network. IEEE Geosci. Remote Sens. Lett. 9, 1–5 (2021)
Duan, Y.L., Huang, H., Tang, Y.X.: Local constraint-based sparse manifold hypergraph learning for dimensionality reduction of hyperspectral image. IEEE Trans. Geosci. Remote Sens. 59(1), 613–628 (2021)
Xiong, F.C., Zhou, J., Qian, Y.T.: Material based object tracking in hyperspectral videos. IEEE Trans. Image Process. 29, 3719–3733 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 2015 International Conference on Learning Representation, pp. 1–14. VenueSan Diego (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegaso (2016)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE, Las Vegaso (2016)
Li, B., Wu, W., Wang, Q., et al.: SiamRPN plus plus: evolution of siamese visual tracking with very deep networks. In: 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4277–4286. IEEE, Salt Lake City (2019)
Bhat, G., Danelljian, M., et al.: Learning discriminative model prediction for tracking. In: 2019 IEEE International Conference on Computer Vision, pp. 6181–6190. IEEE, Seoul (2019)
Wang, Q., Teng, Z., et al.: Learning attentions: residual attentional siamese network for high performance online visual tracking. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4854–4863. IEEE, Long Beach (2018)
Choi, J., Kwon, J., Lee, K.M.: Deep meta learning for real-time target-aware visual tracking. In: 2019 IEEE International Conference on Computer Vision, pp. 911–920. IEEE, Seoul (2019)
He, X., Chen, Y.S., Ghamisi, P.: Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network. IEEE Trans. Geosci. Remote Sens. 58, 3246–3263 (2020)
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638–6646. IEEE, Hawaii (2017)
Choi, J., Chang, H.J., et al.: Context-aware deep feature compression for high-speed visual tracking. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 479–488. IEEE, Long Beach (2018)
Danelljian, M., Bhat, G., et al.: ATOMAccurate tracking by overlap maximization. In: 2019 IEEE International Conference on Computer Vision, pp. 4655–4664. IEEE, Seoul (2019)
Li, X., Ma, C., et al.: Target-aware deep tracking. In: 2019 IEEE International Conference on Computer Vision, pp. 1369–1378. IEEE, Seoul (2019)
Lukežič, A., Matas, J., Kristan, M.: Target-aware deep tracking. In: 2019 IEEE International Conference on Computer Vision, pp. 1369–1378. IEEE, Seoul (2019)
Dai, K.N., Wang, D., et al.: Visual tracking via adaptive spatially-regularized correlation filters. In: 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4665–4674. IEEE, Salt Lake City (2019)
Xu, T.Y., Feng, Z.H., et al.: Joint group feature selection and discriminative filter learning for robust visual object tracking. In: 2019 IEEE International Conference on Computer Vision, pp. 7949–7959. IEEE, Seoul (2019)
Acknowledgments
Thanks to the National Natural Science Foundation of China under Grant 42071302, the Fundamental Research Funds for the Central Universities under Grant 2020CDCGTM002, the Basic and Frontier Research Programmes of Chongqing under Grant cstc2018jcyjAX0093, and the Innovation Program for Chongqing Overseas Returnees under Grant cx2019144 for funding this work. We would also like to thanks to NIVIDIA Corporation for the support of GPU device.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yiming, T., Yufei, L., Hong, H., Chao, Z., Li, Y. (2021). A Deep Transfer Learning-Based Object Tracking Algorithm for Hyperspectral Video. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_66
Download citation
DOI: https://doi.org/10.1007/978-3-030-87361-5_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87360-8
Online ISBN: 978-3-030-87361-5
eBook Packages: Computer ScienceComputer Science (R0)