Radar-vision fusion for object classification

We propose an object classification system that incorporates information from a video camera and a long-range radar system. Our system operates in two steps. The first step is attention selection, in which the radar guides a selection of a small number of candidate images for analysis by the camera. In the second step, a multiple layer in-place learning network (MILN) is used to distinguish images of different objects. Though it is more flexible in terms of variety of classification tasks, the system currently demonstrates its high accuracy in comparison with others on real-world data of a two-class recognition problem.