Novel Moving-Target Detection Using A Hybrid of RGB Images and LiDAR Point-Clouds

Abstract

Moving objects, especially humans and vehicles, impose very high risks on driverless cars or autonomous driving. Real-time moving-target detection plays a critical role in the future intelligent transportation systems due to public-safety concerns. Recently object tracking and scene analysis using a hybrid of high-resolution RGB images and low-resolution LiDAR point-clouds have been drawing a lot of research interest because fast but precise information rendering can be facilitated thereby. Compared to the conventional object detectors using RGB images, the new target detectors using LiDAR point-clouds can lead to three-dimensional object localization with the crucial depth information RGB images cannot provide, which is very critical to navigation of autonomous vehicles and robots. In this paper, we explore a novel moving-target detection approach which is built upon motion-estimation using RGB images and deep-learning over LiDAR data so that the moving object(s) can be accurately localized in an image frame and the target recognition can be conducted using LiDAR point-clouds. Our proposed new scheme can benefit from both tracking-accuracy brought by RGB images and sufficient training-data together with low time-complexity brought by LiDAR data. Preliminary experimental results have shown that our proposed scheme can automatically identify and track moving human-objects in the scene successively.