In this paper we introduce AIT’s Smart Sensor Technology platform, developed within the FOLDOUT project (Through-foliage detection, including in the outermost regions of the EU). The novel platform is part of FOLDOUT’s solution for ground based border surveillance, and in particular detection of foliage penetration. The platform combines a high resolution (4K) RGB camera, a thermal camera, as well as a 30x zoom (2K) NIR low-light camera. It is designed to work day and night under strongly varying weather conditions, it is transportable and can be deployed in remote areas, as well as being self-sufficient if operated by battery. The typical sensor range is about 100-200m which can be adapted per specific usage. The platforming thobe used for surveillance of dedicated areas or by providing an open interface to be integrated into whole surveillance systems in which ground-based sensors are combined with high altitude sensors to provide both close and wide area coverages. The platform provides state-of-the-art AI-based detection algorithms for object detection and classification, for both RGB and thermal images. The individual detections are further fused by establishing coincidence in time and space between the individual detectors, where sensor is geo-localized for accurate target localization and visualization on a map of the area to be monitored.
Foliage penetration is an unsolved important part of border surveillance of remote areas between regular border crossing points. Detecting penetrating objects (e.g., persons and cars) through dense foliage in various climate conditions using visual sensors is prone to high fault rates. Through-foliage scenarios contain an unprecedented amount of occlusion—in fact, they often contain fragmented occlusion (for example, looking through the branches of a tree). Current state-of-the-art detectors based on deep learning perform inadequately under moderate-to-heavy fragmented occlusion. The FOLDOUT project builds a system that combines various sensors and technologies to tackle this problem. Consequently, a hyperspectral sensor was investigated due to its extended spectral bandwidth, beyond the range of typical RGB sensors, where vegetation exhibits pronounced reflectance. Due to the poor performance of deep learning approaches in through-foliage scenarios, a novel background modeling-based detection approach was developed, dedicated to the characteristics of the hyperspectral sensor, namely strong correlations between adjacent spectral bands and high redundancy. The algorithm is based on local dimensional reduction, where the principal subspace of each pixel is maintained and adapted individually over time. The successful application of the proposed algorithm is demonstrated in a through-foliage scenario comprised of heavy fragmented occlusion and a highly dynamical background, where state-of-the-art deep learning detectors perform poorly.
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