This paper presents an experimental study on 3D person localization (i.e. pedestrians, cyclists) in traffic scenes, using monocular vision and LiDAR data. We first analyze the detection performance of two top-ranking methods (PointPillars and AVOD) on the KITTI benchmark, with respect to varying Intersection over Union (IoU) settings and the underlying parameters of 3D bounding box location, extent and orientation. Given that the KITTI dataset contains relatively few 3D person instances, we also consider the new EuroCity Persons 2.5D (ECP2.5D) dataset, which is one order of magnitude larger. We perform domain transfer experiments between the KITTI and ECP2.5D datasets, to examine how these datasets generalize with respect to each other.
An Experimental Study on 3D Person Localization in Traffic Scenes
2020 IEEE Intelligent Vehicles Symposium (IV) ; 1813-1818
2020-10-19
1728572 byte
Conference paper
Electronic Resource
English
AN EXPERIMENTAL STUDY ON 3D PERSON LOCALIZATION IN TRAFFIC SCENES
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