Abstract
Object tracking in spherical panoramic videos is an important yet less studied problem. Besides the common challenges in planar video tracking, panoramic video tracking should address the issues of image distortion and split caused by the sphere-to-plane projection. In this paper, we build a panoramic object tracking dataset, namely POT, containing 40 fully annotated spherical panoramic videos, which are annotated on the pixel level. Moreover, we propose a simple yet effective panoramic tracking framework based on local field-of-view projection, which considers the spherical geometry explicitly and performs object tracking on the tangent plane. Our framework can be easily integrated with well-studied planar video trackers. Besides, we boost five mainstream trackers with our framework. Comprehensive experimental results on the POT dataset demonstrate that the proposed framework is well behaved in panoramic object tracking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.: Staple: Complementary learners for real-time tracking. In: CVPR (2016)
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
Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: ECO: efficient convolution operators for tracking. In: CVPR (2017)
Delforouzi, A., Tabatabaei, S.A.H., Shirahama, K., Grzegorzek, M.: Unknown object tracking in 360-degree camera images. In: ICPR (2016)
Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: Learning SO(3) equivariant representations with spherical CNNs. In: ECCV (2018)
Fan, H., et al.: LaSOT: a high-quality benchmark for large-scale single object tracking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019
Guan, H., Smith, W.A.: BRISKS: binary features for spherical images on a geodesic grid. In: CVPR (2017)
Huang, L., Zhao, X., Huang, K.: GOT-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. (2019)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. TPAMI 34(7), 1409–1422 (2011)
Kiani Galoogahi, H., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: A benchmark for higher frame rate object tracking. In: The IEEE International Conference on Computer Vision (ICCV), October 2017
Kristan, M., et al.: The visual object tracking VOT2015 challenge results. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, December 2015
Li, A., Lin, M., Wu, Y., Yang, M., Yan, S.: NUS-PRO: a new visual tracking challenge. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 335–349 (2016)
Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.H.: Learning spatial-temporal regularized correlation filters for visual tracking. In: CVPR (2018)
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
Liang, P., Blasch, E., Ling, H.: Encoding color information for visual tracking: Algorithms and benchmark. IEEE Trans. Image Process. 24(12), 5630–5644 (2015)
Liu, K.C., Shen, Y.T., Chen, L.G.: Simple online and realtime tracking with spherical panoramic camera. In: ICCE (2018)
Liu, L., Yan, Z., Liu, Q.: Panoramic visual tracking based on adaptive mechanism. JVCIR 57, 99–106 (2018)
Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: The European Conference on Computer Vision (ECCV) (2018)
Su, Y.C., Grauman, K.: Learning spherical convolution for fast features from 360 imagery. In: NIPS (2017)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013
Xiao, J., Ehinger, K.A., Oliva, A., Torralba, A.: Recognizing scene viewpoint using panoramic place representation. In: CVPR (2012)
Zhou, Z., Niu, B., Ke, C., Wu, W.: Static object tracking in road panoramic videos. In: ISM (2010)
Zhu, Y., Zhai, G., Min, X.: The prediction of head and eye movement for 360 degree images. SPIC 69, 15–25 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Pei, S., Chen, Z., Wan, L. (2021). POT: A Dataset of Panoramic Object Tracking. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-88004-0_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88003-3
Online ISBN: 978-3-030-88004-0
eBook Packages: Computer ScienceComputer Science (R0)