A Real-time Multispectral Algorithm for Robust Pedestrian Detection
Vu Hiep Dao, Hieu Mac, Duc Tran · 2021
Low light conditions are known to create a notable challenge to the applicability of deep learning in a wide variety of computer vision applications. In this paper, we develop a detection method for real-time multispectral pedestrians that fuses color image (i.e., red-green-blue or RBG) with thermal image to provide a reliable object vision. Such combination is achieved using the confidence scores that are computed based on the illumination measure of a given input image. We evaluate the proposed algorithm on KAIST dataset. Such method is observed to give a 34.11% Log Average Miss Rate, operate in real-time, and thus, being ready to deploy in practice.