This paper describes a real-time system for detecting people in infrared video taken by a re-locatable camera tower
suitable for border monitoring. Wind effects cause the camera to sway, so typical background modeling techniques
prove difficult to apply. Instead, detection is performed using a supervised classifier over a set of seven Scale Invariant
Image Moments. Blobs images are generated with a simple application of thresholding and dilation, yielding a set of
possible targets. For each potential target, the Scale Invariant Moments are computed and classified as "Person" or
"Non-Person." We present three methods for training the classifier: Linear Discriminant Analysis (LDA), Quadratic
Discriminant Analysis (QDA), and a two-layer Neural Network (NN). We compare the accuracy for the three methods.
Results are presented for sample videos, showing acceptable accuracy while maintaining real time throughput. The key
advantages of this method are real-time performance and tolerance of random ego motion.
In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR)
employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR).
Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the
results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and
micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational
capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged
in deriving a high probability of detection (Pd) while maintaining a low probability of false alarms (Pfa). Our hypothesis
is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same
scene can raise the Pd and lower the Pfa sufficiently in order to provide a cost-effective ATR capability. This capability
can lead to practical implementations of autonomous, coordinated, multi-UAV operations.
In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm
provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars.
The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief
statement. Our system considers how many frames in the observation interval support each potential classification. A
definable function transforms the observational data into a belief value. The belief value, or opinion, represents the
UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion
is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will
have similar index values in the evidence cache. The evidential reasoning system combines observations of similar
spatial indexes, discounting older observations based upon a parameterized information aging function. We employ
Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all
observations that an object of a given class exists in a given region.
Information overload and cluttered user interfaces can lead to decreased situational awareness and lowered performance
of human operators. Irrelevant data increases searching times for tasks requiring the identification of threats, causing
delayed decisions. Cognitive burden on the user increases as displays become more cluttered, which results in increased
operator stress leading to poor decision-making ability. To address this issue, we have designed an intelligent agentbased
system for the automatic de-cluttering of a representative net-centric interface designed for controlling multiple
unmanned aerial vehicles (UAVs) by a single operator. Our concept is called ARID, for Agent-based Reduction of
Information Density. The ARID hypothesis is that intelligent agents can improve operator performance by deemphasizing
those aspects of a display that can be inferred as less-important to the mission goals.
ARID agents receive information about the world via data feeds provided by various net-centric sources. Each agent has
an understanding of the user interface symbols that are used to represent various entities, terrain features, and zones. The
agent also is provided with a mission goal which is used for inferring the relevance of a given symbol to the success of
the mission goal. First level facts, such as spatial relationships, are calculated by supporting agents and assigned a BDU
(belief/disbelief/uncertainty) value. A dynamic set of rules provides an inference mechanism by which an agent can infer
new facts from the given assertions. We have developed a Subjective Logic-based Evidential Reasoning Network that
explicitly deals with belief and uncertainty in the knowledge base, and is used to derive a relevancy belief for every UI
symbol in the map display. Subjective Logic is used to combine values when different sources provide different results
for the same symbol. User Interface agents apply the results of the relevancy beliefs and transform the display to
minimize the apparent clutter caused by less relevant elements. Two transformations, transparency and grouping, are
used in the current implementation.
We present a system for UAV automated landing that requires minimal landing site preparation, no additional
electronics, and no additional aircraft equipment of any kind. This is a Joint-UAV solution that will work equally well
for land-based aircraft and for shipboard recoveries. Our proposed system requires only a simple target that can be
permanently painted on a runway, laid out in a roll-up mat, or potentially denoted with chemical lights for night
operations. Its appearance is unique when seen from the optimal approach path, and from other angles its perspective
distortion indicates the necessary correction. By making continual adjustments based on this feedback, the plane can
land in a small area at the desired angle. We time the pre-touchdown flare using only a 2D visual reference. Assuming a
constant closing speed, we can estimate the time to contact and initiate a controlled flare at a predetermined interval.
KEYWORDS: Data modeling, Data fusion, Probability theory, Information fusion, Systems modeling, Computing systems, Computer architecture, Logic, Mathematical modeling, Prototyping
This paper presents a reasoning system that pools the judgments from a set of inference agents with information
from heterogeneous sources to generate a consensus opinion that reduces uncertainty and improves knowledge
quality. The system, called Collective Agents Interpolation Integral (CAII), addresses a high level data fusion
problem by combining, in a mathematically sound manner, multi-models of inference in knowledge intensive
multi agent architecture. Two major issues are addressed in CAII. One is the ability of the inference mechanisms
to deal with hybrid data inputs from multiple information sources and map the diverse data sets to a uniform
representation in an objective space of reasoning and integration. The other is the ability of the system
architecture to allow the continuous and discrete outputs of a diverse set of inference agents to interact, cooperate,
and integrate.
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