Evaluation of the Fusion of Visible and Thermal Image Data for People Detection with a Trained People Detector

摘要

People detection surely is one of the hottest topics in Computer Vision. In this work we propose and evaluate the fusion of thermal images and images from the visible spectrum for the task of people detection. Our main goal is to reduce the false positive rate of the Implicit Shape Model (ISM) object detector, which is commonly used for people detection. We describe five possible methods to integrate the thermal data into the detection process at different processing steps. Those five methods are evaluated on several test sets we recorded. Their performance is compared to three baseline detection approaches. The test sets contain data from an indoor environment and from outdoor environments at days with different ambient temperatures. The data fusion methods decrease the false positive rate especially on the outdoor test sets.