Person Detection in Thermal Images using Deep Learning
Erik Valldor · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2018
Deep learning has achieved unprecedented results in many image analysis tasks. Long-wave infrared (thermal) images is still a little- explored area of application, and is the main subject of investigation in this thesis. To this end, a case study is performed where the goal is to detect persons in infrared images using deep learning. Two different deep learning based approaches are implemented and benchmarked against a baseline cascaded classifier. Due to the large amount of unlabelled data available, an autoencoder setup is used to pretrain the deep learning based detectors. One of the detectors greatly outperforms the baseline, while the other (an experimental approach) lagged slightly behind the baseline. The main difficulty concerning the ability of the detectors to generalize was determined to be the wide dynamic range of infrared images, together with the many different contrast situations that can occur due to weather and ambient temperature.