Fusion of Heterogenous Sensor Data in Border Surveillance

Abstract

Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.

A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments

“…The main limitation of this approach lies in the fact that these sensors raise privacy issues as they are very invasive. In addition, hardware and implementation costs are quite high, which represents a further drawback to the use of this multi-sensor fusion technology [ 18 , 19 ]. Recently, LiDAR sensors have been widely used to localize people in an indoor environment.…”