Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here we propose a mid-level attribute in the form of multidimensional template, or tensor, using Local Steering Kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e.g., histograms in HOG). Our second contribution is the introduction of a new image similarity kernel in the popular maximum margin framework of support vector machines that results in a relatively short and simple training phase for building a rigid pedestrian detector. Our third contribution is to replace the sluggish but de facto sliding window based detection methodology with multichannel discrete Fourier transform, facilitating very fast and efficient pedestrian localization. The experimental studies on publicly available thermal infrared images justify our proposals and model assumptions. In addition, the proposed work also involves the release of our in-house annotations of pedestrians in more than 17000 frames of OSU Color Thermal database for the purpose of sharing with the research community.

本文提出了一种新的基于局部斯特林核（LSK）的多维模板或张量形式的中级属性，以便更好地检测远红外图像中的行人，并引入了一种新的图像相似度核，以在支持向量机的最大边缘框架内进行训练。我们的第三个贡献是采用多通道离散傅里叶变换来替代滑动窗口法进行行人定位，取得了很好的实验结果。同时，我们还将用于研究为目的的OSU彩色热数据库中的行人注释与研究社区共享。

线性支持张量机：热红外图像中的行人检测