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19 May 2009 Detection and tracking of humans with a sparse network of LIDAR sensors
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The applicability of a sparse sensor network with only two sensor nodes and a small number of directional LIDAR sensors to detect and track humans in an area of surveillance is investigated. The detection and tracking performances are evaluated for various positions of the two nodes as a function of the number of sensors per node and the sensor beamwidths. A quality factor incorporating the area coverage ratio and the position error is introduced to find the best network configuration with a minimal number of sensors yielding a position accuracy sufficient for the task at hand. Extensive simulations and measurements with two laserscanners to emulate the LIDAR sensors were carried out for straight trajectories uniformly distributed over the area of surveillance. In order to improve the tracking performance, we used a Kalman filter based approach. As in our application a spatial mean RMS position error of approx. 0.6 m is sufficient, each of the two sensor nodes must be equipped with 4 LIDAR sensors with a -3dB-beamwidth of 12°.
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Heinrich Ruser, Konrad Wenzl, and Christian Kargel "Detection and tracking of humans with a sparse network of LIDAR sensors", Proc. SPIE 7352, Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing, 73520A (19 May 2009);

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