Fully Convolutional Region Proposal Networks for Multispectral Person Detection

Daniel Konig, Michael Adam, Christian Jarvers, Georg Layher, Heiko Neumann, Michael Teutsch; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 49-56

Multispectral images that combine visual-optical and infrared image information are a promising source of data for automatic person detection. Especially in automotive or surveillance applications, challenging conditions such as insufficient illumination or large distances between camera and object occur regularly and can affect image quality. This leads to weak image contrast or low object resolution. In order to detect persons under such conditions, we apply deep learning for fusing the VIS and IR information in multispectral images. We present a novel multispectral Region Proposal Network that is built up on the pre-trained very deep convolutional network VGG-16. The proposals of this network are evaluated using a Boosted Decision Trees classifier in order to reduce potential false positive detections. With a log-average miss rate of 29.83% on the reasonable test set of the KAIST Multispectral Pedestrian Detection Benchmark, we improve the current state-of-the-art by about 18%.