Infrared Target Detection Based on Temporal-Spatial Domain Fusion

Bing Wen Chen; Shi Long Liu

doi:10.4028/www.scientific.net/AMR.1044-1045.1186

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1044-1045Infrared Target Detection Based on...

Infrared Target Detection Based on Temporal-Spatial Domain Fusion

Abstract:

In order to improve the accuracy and stability of infrared target detection, a novel moving target detection approach based on temporal-spatial domain fusion is presented. A multi-level spatial-temporal median filter is utilized to extract the background frame, with which the background clutters are suppressed by using the background subtraction technique. Then a local weighted operator is applied to enhance the targets. Lastly, the otsu thresholding algorithm is utilized to detect the targets. Experimental results demonstrate that the proposed approach is capable of detecting infrared moving targets effectively for F1 measurement up to 92.8%.

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Periodical:

Advanced Materials Research (Volumes 1044-1045)

Pages:

1186-1189

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1044-1045.1186

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Online since:

October 2014

Authors:

Bing Wen Chen*, Shi Long Liu

Keywords:

Local Weighted Operator, Multi-Level Spatial-Temporal Median Filter, Target Detection

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References

[1] W. Davis James and V. Sharma: Background subtraction in thermal imagery using contour saliency. International journal of Computer Vision, vol. 2, pp.161-181 (2007).

DOI: 10.1007/s11263-006-4121-7

Google Scholar

[2] Z. Li, W. Bo, and N. Ram: Pedestrian detection in infrared imaged based on local shape features. Proc. IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, pp.1-8 (2007).

DOI: 10.1109/cvpr.2007.383452

Google Scholar

[3] H. Stephen O and F. Amber: Detecting people in IR border surveillance video using scale invariant image moments. Proc. Conference on Optical Pattern Recognition, SPIE Press, pp.1-6 (2009).

DOI: 10.1117/12.818905

Google Scholar

[4] B. Fida Ei, T. Bouwmans, and V. Bertrand: Fuzzy foreground detection for infrared videos. Proc. IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, pp.1-6 (2008).

DOI: 10.1109/cvprw.2008.4563057

Google Scholar

[5] N. Otsu: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, pp.62-66 (1979).

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[6] K. Kyunqnam, H. Thanarat, C. and David Harwood: Real-time foreground-background segmentation using codebook model. Image segmentation. Vol. 11, pp.172-185 (2005).

DOI: 10.1016/j.rti.2004.12.004

Google Scholar

[7] M. Lucia and P. Alfredo: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing. Vol. 7, pp.1168-1177 (2008).

DOI: 10.1109/tip.2008.924285

Google Scholar