Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions
Filip Majer1, Zhi Yan2, George Broughton1, Yassine Ruichek2, and Tomáš Krajník1
1AI center, FEE, CTU, Czechia
2CIAD, UTBM, France
In this paper, we present a lifelong-learning multi-sensor system for pedestrian detection in adverse weather conditions. The proposed method combines two people detection pipelines which process data provided by a lidar and an ultra-wideband radar. The outputs of these pipelines are combined not only by means of adaptive sensor fusion, but they can also be used to help one another learn. In particular, the lidar-based detector provides labels to the incoming radar data, efficiently training the radar data classifier. In several experiments, we show that the proposed learning-fusion not only results in a gradual improvement of the system performance during routine operation, but also efficiently deals with lidar detection failures caused by thick fog conditions.