Abstract
In this paper, we strive to settle Multi-object tracking (MOT) problem within Air-Traffic-Control (ATC) surveillance videos. The uniqueness and challenges of the specific problem at hand is two-fold. Firstly, the targets within ATC surveillance videos are small and demonstrate homogeneous appearance. Secondly, the number of targets within the tracking scene undergoes severe variations results from multiple reasons. To solve such a problem, we propose a method that combines the advantages of fast association algorithm and local adjustment technique under a general energy minimization framework. Specifically, a comprehensive and discriminative energy function is established to measure the probability of hypothetical movement of targets, and the optimal output of the function yields to the most responsible target state configuration. Extensive experiments prove the effectiveness of our method on this new dataset.
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
Similar content being viewed by others
References
Cao, X., Gao, C., Lan, J., Yuan, Y., Yan, P.: Ego motion guided particle filter for vehicle tracking in airborne videos. Neurocomputing 124(12), 168–177 (2014)
Cao, X., Shi, Z., Yan, P., Li, X.: Tracking vehicles as groups in airborne videos. Neurocomputing 99(1), 38–45 (2013)
Cao, X., Lan, J., Yan, P., Li, X.: Vehicle detection and tracking in airborne videos by multi-motion layer analysis. Mach. Vis. Appl. 23(5), 921–935 (2012)
Cao, X., Wu, C., Lan, J., Yan, P., Li, X.: Vehicle detection and motion analysis in low-altitude airborne video under urban environment. IEEE Trans. Circ. Syst. Video Technol. 21(10), 1522–1533 (2011)
Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1265–1272 (2011)
Dicle C., Sznaier, M., Camps, O.: The way they move: tracking multiple targets with similar appearance. In: IEEE International Conference on Computer Vision (ICCV), pp. 2304–2311 (2013)
Yang T., Li, S.Z., Pan, Q., Li, J.: Real-time multiple objects tracking with occlusion handling in dynamic scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 970–975 (2005)
Shu, G., Dehghan, A., Oreifej, O., Hand, E., Shah, M.: Part-based multiple-person tracking with partial occlusion handling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1815–1821 (2012)
Li, Z., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, pp. 1–8 (2008)
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. PAMI 33(9), 1806–1819 (2011)
Brendel, W., Amer, M.R., Todorovic, S.: Multi-object tracking as maximum weight independent set. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1273–1280 (2011)
Roshan Zamir, A., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 343–356. Springer, Berlin (2012). doi:10.1007/978-3-642-33709-3_25
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Proc. 2008(1), 1–10 (2008)
Acknowledgement
This paper is supported by the National Science Fund for Distinguished Young Scholars (Grant No. 61425014), the National Natural Science Foundation of China (Grant No. 91538204) and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61521091).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Chen, S., Jiang, X. (2016). Multi-object Tracking Within Air-Traffic-Control Surveillance Videos. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-3476-3_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3475-6
Online ISBN: 978-981-10-3476-3
eBook Packages: Computer ScienceComputer Science (R0)