Efficient Radar Deep Temporal Detection in Urban Traffic Scenes

This paper explores object detection on radar range-Doppler map. Most of the radar processing algorithms are proposed for detecting objects without classifying. Meanwhile, these approaches neglect the useful information available in the temporal domain. To address these problems, we propose an online radar deep temporal detection framework by frame-to-frame prediction and association with low computation. The core idea is that once an object is detected, its location and class can be predicted in the future frame to improve detection results. The experiment results illustrate this method achieves better detection and classification performance, and shows the usability of radar data for traffic scenes.