夜间复杂场景下红外图像行人检测算法研究

Infrared Pedestrian Detection in Complex Night Scenes

针对夜间红外图像中行人与背景灰度差异小且存在遮挡等问题,提出了一种夜间复杂场景下的红外行人检测算法。首先利用行人语义融合方法生成对目标全覆盖的显著图,与原图融合得到感兴趣区域,然后构造基于改进的方向梯度直方图特征的两分支分类器,同时提出一种遮挡判别算法,根据分类器模糊分数判断是否遮挡,设计一种头部模板实现最终的行人检测。在LSI远红外行人数据集和自主采集的冬、夏季节夜间行人数据上进行实验,结果表明:在不同环境下,所提出的方法均可快速鲁棒地检测出行人,可较显著地降低漏检率,检测率可达到94.20%。

An infrared pedestrian detection algorithm is proposed to solve the problem of small differences between pedestrians and backgrounds in gray scale images and the occurrence of occlusion in infrared images at night.First,a significant graph with the full coverage of the target is generated by the pedestrian semantic fusion method,and the region of interest is obtained by combining it with the original graph.Then,a two-branch classifier based on the improved histogram of the gradient feature is constructed.The fuzzy score of the classifier is used to determine the occurrence of occlusion and call the head template for the final detection.Experiments based on the LSI far infrared pedestrian dataset and independent datasets of pedestrians captured at night in winter and summer prove that the proposed method is robust and quick in detecting pedestrians under different environments.It can significantly reduce the rate of missed detection and realize a detection rate of 94.20%.