Thermal imaging can play a critical role in surveillance by promising higher robustness to the bad weather and night vision. Human detection and localization are important surveillance tasks for security purposes and maintaining law and order. This paper proposes a novel regression-based method for human detection from thermal infrared images. A fully convolutional regression network is designed to map the human heat signature in the input thermal image to the spatial density maps. The regressed density map is then post-processed for human detection and localization in the image. 25% data holdout validation scheme is used to train and test the proposed regression model using two benchmark thermal image datasets, the autonomous system lab thermal infrared dataset and the Ohio state university thermal pedestrian database. The proposed regression-based method can detect humans with 99.16% precision and 98.69% recall, outperforming the state-of-the-art conventional hand-crafted and CNN-based techniques for human detection from thermal images. Further, the designed fully convolutional regression network has much reduced computational complexity; yet, the detection performance is on par with the state-of-the-art fully convolutional architectures for predicting the density maps for human detection in thermal images.

热成像可以通过承诺对恶劣天气和夜视具有更高的鲁棒性，在监视中发挥关键作用。人体检测和定位是出于安全目的和维护法律和秩序的重要监视任务。本文提出了一种基于回归的新方法，用于从热红外图像中进行人体检测。全卷积回归网络旨在将输入热图像中的人体热特征映射到空间密度图。然后对回归密度图进行后处理，用于图像中的人体检测和定位。25% 数据保持验证方案用于使用两个基准热图像数据集、自治系统实验室热红外数据集和俄亥俄州立大学热行人数据库来训练和测试所提出的回归模型。所提出的基于回归的方法可以以 99.16% 的精度和 98.69% 的召回率检测人类，优于最先进的传统手工和基于 CNN 的热图像人体检测技术。此外，设计的全卷积回归网络大大降低了计算复杂度；然而，检测性能与用于预测热图像中人体检测密度图的最先进的全卷积架构不相上下。