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Proceedings Paper

Person detection and tracking with a 360° lidar system
Author(s): Marcus Hammer; Marcus Hebel; Michael Arens
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Paper Abstract

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks.

In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed.

The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

Paper Details

Date Published: 5 October 2017
PDF: 7 pages
Proc. SPIE 10434, Electro-Optical Remote Sensing XI, 104340L (5 October 2017);
Show Author Affiliations
Marcus Hammer, Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Germany)
Marcus Hebel, Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Germany)
Michael Arens, Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Germany)

Published in SPIE Proceedings Vol. 10434:
Electro-Optical Remote Sensing XI
Gary Kamerman; Ove Steinvall, Editor(s)

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