Feature based person detection beyond the visible spectrum

One of the main challenges in computer vision is the automatic detection of specific object classes in images. Recent advances of object detection performance in the visible spectrum encourage the application of these approaches to data beyond the visible spectrum. In this paper, we show the applicability of a well known, local-feature based object detector for the case of people detection in thermal data. We adapt the detector to the special conditions of infrared data and show the specifics relevant for feature based object detection. For that, we employ the SURF feature detector and descriptor that is well suited for infrared data. We evaluate the performance of our adapted object detector in the task of person detection in different real-world scenarios where people occur at multiple scales. Finally, we show how this local-feature based detector can be used to recognize specific object parts, i.e., body parts of detected people.