Track-Cued Radar Point Cloud Target Classification

Abstract

A novel temporal-spatial object classification neural network model is proposed to improve the classification capability of tracked objects. It takes queued points of tracked objects using multiple frames as input, utilizes spatial and temporal information from these points for sampling and grouping as well as extracts hierarchical temporal-spatial features for target classification. Experimental results on a proprietary 4D Imaging Radar dataset and open-source 2D RadarScenes dataset demonstrate that the proposed tracker-cued radar point-cloud target classification method allows the model to learn meaningful appearance and motion features from sparse radar points data, and achieves accurate classification output as compared to a baseline method, while being efficient to run on edge hardware with limited resources.