  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.
