Mean shift based target tracking robust to illuminance variation in infrared image sequences

Hamza Soganci, Aysun Çoban
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Abstract

In this paper, a Mean Shift based target tracking approach for infrared image sequences is proposed. Well known Mean Shift method tracks a target in the image using its color histogram. But this approach can fail in infrared image sequences due to several reasons like illuminance variation. However it is possible to improve the performance of Mean Shift approach by using different features. In this paper a feature that does not depend much on illuminance variations is used. Performance of this approach is compared against the standard Mean Shift approach using several infrared image sequences.
基于平均位移的红外图像序列目标跟踪对照度变化的鲁棒性
提出了一种基于Mean Shift的红外图像序列目标跟踪方法。众所周知的Mean Shift方法是利用图像的颜色直方图来跟踪图像中的目标。但由于照度变化等原因,这种方法在红外图像序列中可能会失败。然而,可以通过使用不同的特征来提高Mean Shift方法的性能。本文采用了一种不太依赖于照度变化的特征。利用多幅红外图像序列,比较了该方法与标准Mean Shift方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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