强背景噪声下红外目标的鲁棒性跟踪算法

doi:10.3969/j.issn.1001-2400.2010.06.021

Abstract

Abstract:

A tracking algorithm for the Infrared target is proposed that is the combination of edge detection and the improved Mean Shift method. After the edge of the original infrared image is detected roughly, the non-linear edge detection algorithm is presented that eliminates the most original image noise and could lead to a high-quality image. Based on this image, the improved Mean-Shift algorithm that focuses on renewing the target model, background-weightedness of the target template and Kernal Function-weightedness of the selected target region is applied to implement quick-tracking of a fast-moving target so that the algorithm is not sensitive to moving background noise, and thus it improves tracking procedure stability and robustness to background noise of the algorithm. Experimental results show that the combination of the non-linear edge detection algorithm and Mean Shift tracking algorithm not only reduces the operand of algorithms and improves the tracking speed, but also has a strong robustness to background noise.

Key words: infrared target, edge detection, Mean Shift algorithm, target-tracking

GAO Guo-wang;LIU Shang-qian;QIN Han-lin. Robustness-tracking algorithm for the infrared target under complex background noise[J].J4, 2010, 37(6): 1098-1102.

References

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Robustness-tracking algorithm for the infrared target under complex background noise

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