Full Text:   <2420>

Summary:  <1607>

CLC number: TP391

On-line Access: 2016-11-07

Received: 2016-04-25

Revision Accepted: 2016-08-09

Crosschecked: 2016-10-09

Cited: 0

Clicked: 5836

Citations:  Bibtex RefMan EndNote GB/T7714


Jing-li Gao


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.11 P.1176-1185


Detecting slowly moving infrared targets using temporal filtering and association strategy

Author(s):  Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   gjl991@163.com, wencl@hdu.edu.cn, zjbao@zju.edu.cn, liumeiqin@zju.edu.cn

Key Words:  Temporal target detection, Slowly moving targets, Graph matching, Target association

Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu. Detecting slowly moving infrared targets using temporal filtering and association strategy[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1176-1185.

@article{title="Detecting slowly moving infrared targets using temporal filtering and association strategy",
author="Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Detecting slowly moving infrared targets using temporal filtering and association strategy
%A Jing-li Gao
%A Cheng-lin Wen
%A Zhe-jing Bao
%A Mei-qin Liu
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 11
%P 1176-1185
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601203

T1 - Detecting slowly moving infrared targets using temporal filtering and association strategy
A1 - Jing-li Gao
A1 - Cheng-lin Wen
A1 - Zhe-jing Bao
A1 - Mei-qin Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 11
SP - 1176
EP - 1185
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601203

The special characteristics of slowly moving infrared targets, such as containing only a few pixels, shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering, temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets, and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bae, T., 2014. Spatial and temporal bilateral filter for infrared small target enhancement. Infrared Phys. Technol., 63:42-53.

[2]Chen, C.L., Li, H., Wei, Y., et al., 2014. A local contrast method for small infrared target detection. IEEE Trans. Geosci. Remote Sens., 52(1):574-581.

[3]Chen, Z., Wang, X., Sun, Z., et al., 2016. Motion saliency detection using a temporal Fourier transform. Opt. Laser Technol., 80:1-15.

[4]Comaniciu, D., Ramesh, V., Meer, P., 2000. Real-time tracking of non-rigid objects using mean shift.Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.142-149.

[5]Deng, L., Zhu, H., Tao, C., et al., 2016. Infrared moving point target detection based on spatial-temporal local contrast filter. Infrared Phys. Technol., 76:168-173.

[6]Deshpande, S.D., Meng, H.E., Venkateswarlu, R., et al., 1999. Max-mean and max-median filters for detection of small targets.Proc. SPIE, p.74-83.

[7]Dong, X., Huang, X., Zheng, Y., et al., 2014. Infrared dim and small target detecting and tracking method inspired by human visual system. Infrared Phys. Technol., 62:100-109.

[8]Gao, C., Meng, D., Yang, Y., et al., 2013. Infrared patch-image model for small target detection in a single image. IEEE Trans. Image Process., 22(12):4996-5009.

[9]Gao, J., Wen, C., Liu, M., 2015. Low-speed small target detection based on SVD and superposition. J. Shanghai Jiao Tong Univ., 49(6):876-883 (in Chinese).

[10]Kim, S., Sun, S.G., Kim, K.T., 2014. Highly efficient supersonic small infrared target detection using temporal contrast filter. Electron. Lett., 50(2):81-83.

[11]Li, Y., Li, P., Shen, Q., 2014. Real-time infrared target tracking based on l1 minimization and compressive features. Appl. Opt., 53(28):6518-6526.

[12]Liu, D., Li, Z., Wang, X., et al., 2015. Moving target detection by nonlinear adaptive filtering on temporal profiles in infrared image sequences. Infrared Phys. Technol., 73:41-48.

[13]Liu, R., Li, X., Han, L., et al., 2013. Track infrared point targets based on projection coefficient templates and non-linear correlation combined with Kalman prediction. Infrared Phys. Technol., 57:68-75.

[14]Miezianko, R., 2006. IEEE OTCBVS WS Series Bench: Terravic Research Infrared Database.Available from http://vcipl-okstate.org/pbvs/bench/Data/05/download.html.

[15]Silverman, J., Mooney, J.M., Caefer, C.E., 1996. Temporal filters for tracking weak slow point targets in evolving cloud clutter. Infrared Phys. Technol., 37(6):695-710.

[16]Taj, M., Maggio, E., Cavallaro, A., 2006. Multi-feature graph-based object tracking.Proc. 1st Int. Evaluation Workshop on Classification of Events, Activities and Relationships, p.190-199.

[17]Wang, Z., Tian, J., Liu, J., et al., 2006. Small infrared target fusion detection based on support vector machines in the wavelet domain. Opt. Eng., 45(7):076401.

[18]Wang, Z., Ma, Y., Wang, L., 2013. Assessment of threat degree for LSS target in air defense operation. Shipboard Electron. Countermeas., 36(6):103-105 (in Chinese).

[19]Yan, X., Wu, X., Kakadiaris, I.A., et al., 2012. To track or to detect? An ensemble framework for optimal selection.Proc. 12th European Conf. on Computer Vision, p.594-607.

[20]Yang, Y., Wu, J., Zheng, W., 2012. Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 13(7):534-543.

[21]Zhang, F., Li, C., Shi, L., 2005. Detecting and tracking dim moving point target in IR image sequence. Infrared Phys. Technol., 46(4):323-328.

[22]Zhang, J., Guo, H., 2012. Net cast interception system research aimed at low small slow target. Comput. Eng. Des., 33(7):2874-2878 (in Chinese).

[23]Zhang, J., Li, Q., Cheng, N., et al., 2013. Nonlinear path-following method for fixed-wing unmanned aerial vehicles. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(2):125-132.

[24]Zhang, Y., Xin, Y., Zhang, C., 2010. An algorithm based on temporal and spatial filters for infrared weak slow moving point target detection. Acta Photon. Sin., 39(11):2049-2054 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE