Rich feature hierarchies from omni-directional RGB-DI information for pedestrian detection
In this paper, we propose an omni-directional pedestrian detection method from color, depth, and laser intensity (RGB-DI) information by fusing two different sensors, catadioptric camera and 3D LiDAR scanner. Our method is based on the use of Regions with Convolutional Neural Network (R-CNN) features, which is known as the state-of-the-art object detection method at this moment. The problem of R-CNN is that it takes long computation times over omni-directional searches. By fusing two sensors, we reduced the number of candidate regions and the whole computation time under half, and achieved better performances in the outdoor environment.