基于贝叶斯框架鲁棒张量分解模型的红外小目标检测算法

Yihua Tan, Zhi Li, Yuan Xiao, Na Liu
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引用次数: 1

摘要

考虑到背景具有高相关性和低秩的特点,而前景目标保持稀疏性,可以进一步提高红外视频中的小目标检测。本文提出了一种基于贝叶斯框架的红外小目标检测算法。将视频序列的三维张量结构分解为低秩背景、稀疏前景和噪声。三部分对应的概率模型组成一个贝叶斯网络,用变分贝叶斯推理求解。最后,利用分离的稀疏分量进一步检测目标。实验结果表明,该方法适用于红外小目标的检测,具有较好的检测精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Infrared Small Target Detection Algorithm Based on Robust Tensor Decomposition Model within Bayesian Framework
Small targets detection in infrared video can be further improved by considering that the background has high correlation and low rank characteristics while foreground objects maintain sparsity. In this paper, a new infrared small target detection algorithm within Bayesian framework is proposed. A three-dimensional tensor structure of the video sequence is supposed to be decomposed into low rank background, sparse foreground and noise. The corresponding probabilistic models for the three parts form a Bayesian network which is solved by using variational Bayesian inference. Finally, the isolated sparse component is utilized for further target detecition. Experimental results show that the proposed method is suitable for the detection of small infrared target with good detection accuracy and robustness.
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