To track walking persons inside a surveillance area we use LIDAR (LIght Detection And Ranging) sensors with a
small number N of spatially stationary LIDAR beams in order to keep the sensor costs to a minimum. To achieve
high target detectability and tracking performance, the coverage of the surveillance area by the N LIDAR beams
must be large, which is why the beamwidth is to be set to a practically feasible maximum. As a result, the
lateral localization error inside these wide LIDAR beams is high while the area of surveillance can still not be
entirely covered by LIDAR beams. Thus, the accurate tracking of persons walking inside the area of surveillance
is challenging. In the classical tracking approach, the axial position of a target inside a LIDAR beam is obtained
from time-of-
ight measurements. However, the lateral deviation of the target position from the optical beam
axis remains unknown. In this paper, a novel approach to reduce the lateral localization error is proposed and
investigated. From consecutively measured (axial) distances to the target while it moves through a LIDAR beam
the target velocity vector is estimated and used as observation for a Kalman-based tracking algorithm. The
localization and tracking performances of the novel approach are determined and compared with those of the
classical approach.
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.
Surveillance is an important application of sensor networks. In this paper it is demonstrated how a sparse
network of stationary infrared (IR) sensors with highly directional, stationary beam patterns based on the
LIDAR principle can be used to reliably track persons. Due to the small number of sensors and their narrow
beam patterns a significant portion of the area to be surveilled is not directly assessed by the sensors. To
nonetheless achieve reliable tracking of moving targets in the entire area to be monitored, we employ the most
appropriate sensor network configuration and propose a probabilistic tracking approach. The behavior of a
person moving through the area of observation is classified as "normal" or "abnormal" depending upon the
trajectory and motion dynamics. The classification is based on a linear Kalman prediction.
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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