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Proceedings Paper

Multiple hypothesis tracking of two persons using a network of lidar sensors with stationary and directional beams
Author(s): Konrad Wenzl; Heinrich Ruser; Christian Kargel
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Paper Abstract

A sensor network based on the LIDAR (LIght Detection And Ranging) principle is investigated in order to track persons inside a surveillance area and be able to identify security-relevant behavior. In order to minimize the overall sensor network complexity, power consumption and costs, we recently investigated the network topology based on a quality measure in terms of the number of nodes, measurement distance, width of the LIDAR beams and localization as well as classification performance. As a result, stationary beams with rather small opening angles of up to a few degrees are a good compromise. Since certain regions of the surveillance area are not directly assessed by the LIDAR beams, the accurate tracking of a target throughout the entire area of surveillance is challenging. We demonstrated that tracking based on a Kalman filter approach can nevertheless deliver satisfactory results for a single person inside the surveillance area. In this paper we focus on the task of reliably tracking two persons inside the surveillance area at the same time. The tracking of multiple moving targets is carried out by applying a Multiple Hypothesis Tracking filter approach. The localization and tracking performances are derived from simulations and experiments carried out with commercial laser scanners. In the near future, the rather expensive laser scanners will be replaced by appropriate LIDAR range finders.

Paper Details

Date Published: 28 April 2010
PDF: 10 pages
Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 77100B (28 April 2010); doi: 10.1117/12.850235
Show Author Affiliations
Konrad Wenzl, Bundeswehr Univ. Munich (Germany)
Heinrich Ruser, Bundeswehr Univ. Munich (Germany)
Christian Kargel, Bundeswehr Univ. Munich (Germany)


Published in SPIE Proceedings Vol. 7710:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010
Jerome J. Braun, Editor(s)

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