Pedestrian detection in infrared images is always a challenging task. Segmentation is an important step of pedestrian detection. An accurate segmentation could provide more information for further analysis. In this paper, an improved Fuzzy C-Means clustering method, which incorporates geometric symmetry information, is proposed for infrared pedestrian segmentation. In the proposed method, symmetry information is introduced by Markov random field theory. Moreover, a new metric is utilized to handle the weak symmetry of pedestrian. In addition, a whole procedure is proposed to extract infrared pedestrians. The experimental results indicate that our method performs better for infrared pedestrian segmentation and obtains better segmentation results compared with other state-of-the-art methods.

中文翻译：

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基于对称信息的红外行人分割模糊聚类


红外图像中的行人检测始终是一项具有挑战性的任务。分割是行人检测的重要步骤。准确的细分可以为进一步分析提供更多信息。本文提出了一种融合几何对称信息的改进的模糊C均值聚类方法，用于红外行人分割。在该方法中，通过马尔可夫随机场理论引入对称信息。此外，利用一种新的度量来处理行人的弱对称性。此外，还提出了提取红外行人的完整过程。实验结果表明，与其他最先进的方法相比，我们的方法在红外行人分割方面表现更好，并获得了更好的分割结果。