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Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding

机译:基于背景减法和局部阈值的红外图像自适应行人检测

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Infrared (IR) imaging is the order of the day with potential real life applications such as surveillance, defence, non-military applications and so on. Low contrast, poor illumination due to capturing devices and moderate to low environmental conditions are the general characterizations of IR images. In addition, the occlusion of objects make the detection more challenging. The objects considered in this paper are pedestrians. A simple and efficient single image handling pedestrian detection method is proposed in this paper. The two major tasks in the proposed method are background subtraction model and local adaptive thresholding. The major contribution of the paper is the adaptive calculation of the required parameters based on the image characteristics. Experiments are conducted on the standard OSU thermal pedestrian database to show the robustness of the proposed method. The proposed method attain detection rate of 90% under various environmental conditions which is superior than the other existing single image handling methods.
机译:红外(IR)成像已成为日常工作中潜在的现实生活应用,例如监视,国防,非军事应用等。低对比度,由于捕获设备导致的照明差以及中等至低环境条件是IR图像的一般特征。另外,物体的遮挡使检测更具挑战性。本文考虑的对象是行人。提出了一种简单有效的单图像行人检测方法。该方法的两个主要任务是背景减法模型和局部自适应阈值。本文的主要贡献是根据图像特征对所需参数进行自适应计算。在标准OSU热步行人数据库上进行了实验,以显示所提出方法的鲁棒性。所提出的方法在各种环境条件下均达到90%的检测率,优于其他现有的单个图像处理方法。

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