The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 131–138, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-131-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 131–138, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-131-2021

  28 Jun 2021

28 Jun 2021

INFORMATION ACQUISITION ON PEDESTRIAN MOVEMENTS IN URBAN TRAFFIC WITH A MOBILE MULTI-SENSOR SYSTEM

B. Borgmann1,2, M. Hebel1, M. Arens1, and U. Stilla2 B. Borgmann et al.
  • 1Fraunhofer IOSB, Ettlingen, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation. Fraunhofer Center for Machine Learning. Gutleuthausstr. 1, 76275 Ettlingen, Germany
  • 2Photogrammetry and Remote Sensing, Technische Universitaet Muenchen. Arcisstr. 21, 80333 Munich, Germany

Keywords: mobile laser scanning, LiDAR, pedestrian detection, multi-sensor, urban traffic

Abstract. This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR’s capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the detected pedestrians. The approach combines several state-of-the-art machine learning techniques: it uses a neural network and a subsequent voting process to detect pedestrians in LiDAR sensor data. Based on the known geometric constellation of the different sensors and the knowledge of the intrinsic parameters of the cameras, image sections are generated with the respective regions of interest showing only the detected pedestrians. These image sections are then processed with a method for image-based human pose estimation to determine keypoints for different body parts. These keypoints are finally projected from 2D image coordinates to 3D world coordinates using the assignment of the original LiDAR points to a particular pedestrian.