Online Information Augmented SiamRPN
Recently, many Siamese network based object tracking methods have been proposed and have shown good performances. These method give two images to two identical artificial neural networks as the inputs and find the target area based on the similarity measured by the Siamese network. However, the measure used in the Siamese network is based on the offline training, and therefore, easily fail to adapt to online changes. In this paper, we propose to apply a distance measure which considers the relative position between the objects and the histogram information as additional online information. This additional information prevents the tracking to fail when hard negative cases appear in the scene.
KeywordsObject tracking Siamese network Histogram Deep learning
This work was supported by the Technology development Program (S2644388) funded by the Ministry of SMEs and Startups (MSS, Korea) and the Basic Science Research Program through the National Research Foundation of Korea (NRF-2019R1I1A3A01060150).
- 1.Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: Staple: complementary learners for real-time tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1401–1409, June 2016. https://doi.org/10.1109/CVPR.2016.156
- 3.Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014). https://doi.org/10.5244/C.28.65
- 5.Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. CoRR abs/1404.7584 (2014). http://arxiv.org/abs/1404.7584
- 6.Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8971–8980, June 2018. https://doi.org/10.1109/CVPR.2018.00935
- 8.Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4293–4302, June 2016. https://doi.org/10.1109/CVPR.2016.465
- 9.Ning, G., et al.: Spatially supervised recurrent convolutional neural networks for visual object tracking. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4, May 2017. https://doi.org/10.1109/ISCAS.2017.8050867
- 10.Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, June 2016. https://doi.org/10.1109/CVPR.2016.91
- 11.Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015). http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf
- 14.Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware siamese networks for visual object tracking. CoRR abs/1808.06048 (2018). https://arxiv.org/abs/1808.06048