Linear Support Tensor Machine with LSK Channels: Pedestrian Detection in Thermal Infrared Images

Abstract

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 the 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 histogram of oriented gradients). In order to learn the LSK tensor, we introduce a new image similarity kernel following the popular maximum margin framework of support vector machines facilitating a relatively short and simple training phase for building a rigid pedestrian detector. Tensor representation has several advantages, and indeed, LSK templates allow exact acceleration of 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 17 000 frames of OSU color thermal database for the purpose of sharing with the research community.