Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible

Feature detection and description algorithms represent an important milestone in most computer vision applications. They have been examined from various perspectives during the last decade. However, most studies focused on their performance when used on visible band imagery. This modality suffers considerably in poor lighting conditions and notably during night-time. Infrared cameras, which noticed a considerable proliferation in recent years, offer a viable alternative in such conditions. Understanding how the building blocks of computer vision applications behave in this modality would help the community accommodating them. For this reason, we carried out a performance analysis of the most commonly used feature detectors and descriptors beyond the visible. A dataset accounting for the various challenges on these algorithms has been generated. In addition, challenges inherent to the thermal modality have been considered, notably the non-uniformity noise. A comprehensive quantitative investigation into the performance of feature detectors and descriptors is therefore presented. This study would serve to filling the gap in the literature as most analyzes have been based on visible band imagery.