Abstract
Thermal sensors bring a lot of benefits for object tracking due to the robustness to lighting changes over time, which still give a great difficulty to traditional tracking methods. Despite this powerful advantage, the ambiguity due to background clutters and occlusions makes the problem of thermal sensor-based tracking intractable. In this paper, we propose a novel framework for multiple object tracking in livestock breeding spaces using a single thermal sensor. The key idea of the proposed method is to define a new rotatable elliptical motion model, which is suitable for describing complicated motions of animals. Moreover, the proposed local constraint guides our tracker to robustly chase targets even with severe occlusions. Experimental results on various thermal video sequences show the significant improvement for tracking animals compared to other approaches proposed in the literature.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Dell, A.I., et al.: Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29(7), 417–428 (2014)
Branson, K., Belongie, S.: Tracking multiple mouse contours (without too many samples). In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 1, pp. 1039–1046 (2005)
Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Hare, S., et al.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)
Gade, R., Moeslund, T.B.: Thermal cameras and applications: a survey. Mach. Vis. Appl. 25(1), 245–262 (2014)
Berg, A., Ahlberg, J., Felsberg, M.: Channel coded distribution field tracking for thermal infrared imagery. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp. 1248–1256 (2016)
Portmann, J., Lynen, S., Chli, M., Siegwart, R.: People detection and tracking from aerial thermal views. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 1794–1800 (2014)
Zhang, T., Wiliem, A., Hemsony, G., Lovell, B. C.: Detecting kangaroos in the wild: the first step towards automated animal surveillance. In: Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 1961–1965 (2015)
Christiansen, P., Steen, K.A., Jørgensen, R.N., Karstoft, H.: Automated detection and recognition of wildlife using thermal cameras. Sensors 14(8), 13778–13793 (2014)
Kim, W., Cho, Y.B., Lee, S.: Thermal sensor-based multiple object tracking for intelligent livestock breeding. IEEE Access 5, 27453–27463 (2017)
Bishop, G., Welch, G.: An introduction to the Kalman filter. In: SIGGRAPH Course Notes, Los Angeles, pp. 1–81 (2001)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Bradski, G.R.: Real time face and object tracking as a component of a perceptual user interface. In: Proc. 4th IEEE Workshop Appl. Comput. Vis., pp. 214–219 (1998)
Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Proc. 3rd Int. Conf. Comput. Vis., vol. 1, pp. 390–393 (1990)
Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: Proc. European Conf. Comput. Vis., pp. 749–765 (2016)
Berg, A., Ahlberg, J., Felsberg, M.: A thermal object tracking benchmark. In: Proc. IEEE Int. Conf. Adv. Video Signal Surveill., pp. 1–6 (2015)
Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Multi-commodity network flow for tracking multiple people. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1614–1627 (2014)
Li, Y., Huang, C., Nevatia, R.: Learning to associate: HybridBoosted multi-target tracker for crowded scene. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 2953–2960 (2009)
Wu, Y., Lim, J., Yang, M.-H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)
Girshick, R.: Fast R-CNN. In: Proc. IEEE Int. Conf. Comput. Vis., pp. 1440–1448 (2015)
Kristan, M. et al.: The visual object tracking vot2015 challenge results. In: Proc. IEEE Int. Conf. Comput. Vis. Workshop, pp. 1–23 (2015)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Acknowledgements
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries through the Advanced Production Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs under Grant 116056-03.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by M. Kankanhalli.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (avi 12574 KB)
Rights and permissions
About this article
Cite this article
Kim, M., Kim, W. Top-down thermal tracking based on rotatable elliptical motion model for intelligent livestock breeding. Multimedia Systems 26, 509–517 (2020). https://doi.org/10.1007/s00530-020-00658-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-020-00658-5