The ability to passively reconstruct a scene in 3D provides significant benefit to Situational Awareness systems
employed in security and surveillance applications. Traditionally, passive 3D scene modelling techniques, such as Shape
from Silhouette, require images from multiple sensor viewpoints, acquired either through the motion of a single sensor or
from multiple sensors. As a result, the application of these techniques often attracts high costs, and presents numerous
practical challenges. This paper presents a 3D scene reconstruction approach based on exploiting scene shadows, which
only requires information from a single static sensor. This paper demonstrates that a large amount of 3D information
about a scene can be interpreted from shadows; shadows reveal the shape of objects as viewed from a solar perspective
and additional perspectives are gained as the sun arcs across the sky. The approach has been tested on synthetic and real
data and is shown to be capable of reconstructing 3D scene objects where traditional 3D imaging methods fail. Providing
the shadows within a scene are discernible, the proposed technique is able to reconstruct 3D objects that are
camouflaged, obscured or even outside of the sensor's Field of View. The proposed approach can be applied in a range
of applications, for example urban surveillance, checkpoint and border control, critical infrastructure protection and for
identifying concealed or suspicious objects or persons which would normally be hidden from the sensor viewpoint.
KEYWORDS: Sensors, Energy harvesting, Solar energy, Clouds, Energy efficiency, Wind energy, Detection and tracking algorithms, Fusion energy, Vegetation, Surveillance
This paper considers the exploitation of energy harvesting technologies for teams of Autonomous Vehicles (AVs).
Traditionally, the optimisation of information gathering tasks such as searching for and tracking new objects, and
platform level power management, are only integrated at a mission-management level. In order to truly exploit new
energy harvesting technologies which are emerging in both the commercial and military domains (for example the
'EATR' robot and next-generation solar panels), the sensor management and power management processes must be
directly coupled. This paper presents a novel non-myopic sensor management framework which addresses this issue
through the use of a predictive platform energy model. Energy harvesting opportunities are modelled using a dynamic
spatial-temporal energy map and sensor and platform actions are optimised according to global team utility. The
framework allows the assessment of a variety of different energy harvesting technologies and perceptive tasks. In this
paper, two representative scenarios are used to parameterise the model with specific efficiency and energy abundance
figures. Simulation results indicate that the integration of intelligent power management with traditional sensor
management processes can significantly increase operational endurance and, in some cases, simultaneously improve
surveillance or tracking performance. Furthermore, the framework is used to assess the potential impact of energy
harvesting technologies at various efficiency levels. This provides important insight into the potential benefits that
intelligent power management can offer in relation to improving system performance and reducing the dependency on
fossil fuels and logistical support.
Three-dimensional (3D) imaging technologies have considerable potential for aiding military operations in areas such as
reconnaissance, mission planning and situational awareness through improved visualisation and user-interaction. This
paper describes the development of fast 3D imaging capabilities from low-cost, passive sensors. The two systems
discussed here are capable of passive depth perception and recovering 3D structure from a single electro-optic sensor
attached to an aerial vehicle that is, for example, circling a target. Based on this example, the proposed method has been
shown to produce high quality results when positional data of the sensor is known, and also in the more challenging case
when the sensor geometry must be estimated from the input imagery alone. The methods described exploit prior
knowledge concerning the type of sensor that is used to produce a more robust output.
The overall goal of the research project reported here is to create a novel system that can combine input from multiple
passive sensors at different viewpoints (such as uninhabited aerial vehicles) into a single integrated three-dimensional
(3D) view of a scene. This form of intelligent data processing, known as Volume Registration, can further exploit the
available information to enable improved surveillance, reconnaissance and situational awareness, and thus offers
substantial potential benefit to military applications. This paper focuses on the case of multiple sensors onboard UAVs
operating at mid-altitude, and describes two complementary techniques that have been investigated in parallel to address
this challenge. The first of these is depth from disparity, which allows a real-time per-pixel estimation of the distance of
scene objects from the camera; the second is shape from silhouette, which back-projects a segmented version of the
image onto a 3D block of voxels and 'carves' a 3D model over multiple frames. The main steps of each algorithm are
outlined, along with appropriate results, in order to demonstrate how they could form a useful part of a practical Volume
Registration system. A number of possible extensions and improvements to the system architecture are also discussed to
improve the accuracy and efficiency of these techniques, and their applicability to the more complex low-altitude case is
discussed.
The wakes of ships and small surface craft such as fast inshore attack craft (FIAC) can provide crucial intelligence about
their behaviour. The development of image processing algorithms to automatically extract this information from
surveillance imagery represents a challenging task, but one which can provide great benefits for naval security. This
paper reports on research into novel wake detection and analysis techniques to improve the security of naval vessels and
harbour environments, and demonstrates the results of novel methods for extracting the positions, heading, size and
speed of a vessel from imagery through an analysis of these wake components. The wakes of small surface craft are
typically many times larger than the vessel itself and therefore yield a more robust method for detection. Consequently,
this detection technique can be successful even when the vessel is unresolved. These methods can be used to track the
vessels and examine their paths to determine their behaviour. This is achieved through the use of Waterfall Solutions
Ltd's (WS) existing tracking techniques and the development of novel anomaly detection methods. The work
demonstrates the ability to extract vessel properties and infer behaviour from their wakes, which is beneficial in
determining the vessel's degree of threat and intent.
Many image fusion systems involving passive sensors require the accurate registration of the sensor data prior to
performing fusion. Since depth information is not readily available in such systems, all registration algorithms are
intrinsically approximations based upon various assumption about the depth field. Although often overlooked, many
registration algorithms can break down in certain situations and this may adversely affect the image fusion performance.
In this paper, we discuss a framework for quantifying the accuracy and robustness of image registration algorithms
which allows a more precise understanding of their shortcomings. In addition, some novel algorithms have been
investigated that overcome some of these limitations. A second aspect of this work has considered the treatment of
images from multiple sensors whose angular and spatial separation is large and where conventional registration
algorithms break down (typically greater than a few degrees of separation). A range of novel approaches is reported
which exploit the use of parallax to estimate depth information and reconstruct a geometrical model of the scene. The
imagery can then be combined with this geometrical model to render a variety of useful representations of the data.
These techniques (which we term Volume Registration) show great promise as a means of gathering and presenting 3D
and 4D scene information for both military and civilian applications.
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