Estimating the motion of moving targets from a moving platform is an extremely challenging problem in un-manned systems research. One common and often successful approach is to use optical flow for motion estimation to account for ego-motion of the platform and to then track the motion of surrounding objects. However, in the presence of video degradation such as noise, compression artifacts, and reduced frame rates, the performance of
state-of-the-art optical flow algorithms greatly diminishes. We consider the effects of video degradation on two well-known optical flow datasets as well as on a real-world video data. To highlight the need for robust optical flow algorithms in the presence of real-world conditions, we present both qualitative and quantitative results on
these datasets.
Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.
The Joint Force Protection Advanced Security System (JPFASS) is a Department of Defense effort to improve
conventional force protection. It is sponsored and managed by Joint Program Manager - Guardian (JPM-G). The main
objective of JFPASS is to provide an integrated and layered base defense system, which includes data fusion, Command
and Control (C2) nodes, Common Operation Picture (COP) nodes, and full integration of a selected range of robots,
sensors, cameras, weapons, tracking systems, and other C2 systems. The URIM is the main integration tool for several
sensors, cameras, and weapons in JFPASS.
The Universal Resource Interface Module (URIM) is an extremely flexible framework for rapidly integrating new
sensors into the JFPASS. Each sensor system has its own proprietary protocol, which makes integration high cost and
risk. The URIM communicates directly with each sensor system though a protocol module and maintains a generic data
object representation for each sensor. The URIM then performs a translation of the data into a single protocol, in this
case Systems Engineering and Integration Working Group (SEIWG) ICD-0100. With this common protocol the data
can be provided to a data server for publishing. Also, this allows for network control and management of all sensor
systems via any C2 node connected to the data server.
KEYWORDS: Sensors, Polonium, Acoustics, Detection and tracking algorithms, Data analysis, Receivers, Magnetic sensors, Infrared sensors, Defense and security, Data fusion
The FPJE was an experiment to consider the best way to develop and evaluate a system of systems approach to Force
Protection. It was sponsored by Physical Security Equipment Action Group (PSEAG) and Joint Program Manager -
Guardian (JPM-G), and was managed by the Product Manager - Force Protection Systems (PM-FPS). The experiment
was an effort to utilize existing technical solutions from all branches of the military in order to provide more efficient
and effective force protection. The FPJE consisted of four separate Integration Assessments (IA), which were intended
as opportunities to assess the status of integration, automation and fusion efforts, and the effectiveness of the current
configuration and "system" components. The underlying goal of the FPJE was to increase integration, automation, and
fusion of the many different sensors and their data to provide enhanced situational awareness and a common operational
picture.
One such sensor system is the Battlefield Anti-Intrusion System (BAIS), which is a system of seismic and acoustic
unmanned ground sensors. These sensors were originally designed for employment by infantry soldiers at the platoon
level to provide early warning of personnel and vehicle intrusion in austere environments. However, when employed
around airfields and high traffic areas, the sensitivity of these sensors can cause an excessive number of detections.
During the second FPJE-IA all of the BAIS detections and the locations of all Opposing Forces were logged and
analyzed to determine the accuracy rate of the sensors. This analysis revealed that with minimal filtering of detections,
the number of false positives and false negatives could be reduced substantially to manageable levels while using the
sensors within extreme operational acoustic and seismic noise conditions that are beyond the design requirements.
The FPJE was an experiment to consider the best way to create a system of systems in the realm of Force Protection. It
was sponsored by Physical Security Equipment Action Group (PSEAG) and Joint Program Manager - Guardian (JPMG),
and was managed by the Product Manager - Force Protection Systems (PM-FPS). The experiment attempted to
understand the challenges associated with integrating disparate systems into a cohesive unit, and then the compounding
challenge of handling the flow of data into the system and its dispersion to all subscribed Command and Control (C2)
nodes. To handle this data flow we created the DFE based on the framework of the Joint Battlespace Command and
Control System for Manned and Unmanned Assets (JBC2S).
The DFE is a data server that receives information from the network of systems via the Security Equipment Integration
Working Group (SEIWG) ICD-0100 protocol, processes the data through static fusion algorithms, and then publishes the
fused data to the C2 nodes, in this case JBC2S and the Tactical Automated Security System (TASS). The DFE uses only
known concepts and algorithms for its fusion efforts. This paper discusses the analyzed impact of the fusion on C2
nodes displays and in turn on the operators. Also, this paper discusses the lessons learned about networked control
combined with DFE generated automatic response. Finally, this paper discusses possible future efforts and their benefits
for providing the useful operational picture to the operator.
The Family of Integrated Rapid Response Equipment (FIRRE) is an advanced technology demonstration program intended to develop a family of affordable, scalable, modular, and logistically supportable unmanned systems to meet urgent operational force-protection needs and requirements worldwide. The near-term goal is to provide the best available unmanned ground systems to the warfighter in Iraq and Afghanistan. The overarching long-term goal is to develop a fully-integrated, layered force-protection system of systems for our forward deployed forces that is networked with the future force C4ISR systems architecture. The intent of the FIRRE program is to reduce manpower requirements, enhance force-protection capabilities, and reduce casualties through the use of unmanned systems. FIRRE is sponsored by the Office of the Under Secretary of Defense, Acquisitions, Technology and Logistics (OUSD AT&L), and is managed by the Product Manager, Force Protection Systems (PM-FPS), Fort Belvior, VA.
The command-and-control element of FIRRE is the Joint Battlespace Command and Control System (JBC2S) for manned and unmanned assets, which is based upon the Mobile Detection Assessment Response System (MDARS) Multiple Resource Host Architecture (MRHA), modified to operate as a single application program using standard DoD mapping and data distribution services. JBC2S is an evolution of the MRHA that leverages over 10 years of development in unmanned systems command-and-control. It implements the functionality of the MRHA under the dynamically configurable and highly modular architecture of the Multi-Robot Operator Control Unit (MOCU). JBC2S is a network-centric, geospatial command and control system that allows the field commander and above to plan and execute missions utilizing multiple and disparate manned and unmanned assets. It utilizes standard map formats (GeoTIFF, DNC, CADRG) for displaying map data and for tracking asset placement and movement.
The Family of Integrated Rapid Response Equipment (FIRRE) is an advanced technology demonstration program intended to develop a family of affordable, scalable, modular, and logistically supportable unmanned systems to meet urgent operational force protection needs and requirements worldwide. The near-term goal is to provide the best available unmanned ground systems to the warfighter in Iraq and Afghanistan. The overarching long-term goal is to develop a fully-integrated, layered force protection system of systems for our forward deployed forces that is networked with the future force C4ISR systems architecture. The intent of the FIRRE program is to reduce manpower requirements, enhance force protection capabilities, and reduce casualties through the use of unmanned systems. FIRRE is sponsored by the Office of the Under Secretary of Defense, Acquisitions, Technology and Logistics (OUSD AT&L), and is managed by the Product Manager, Force Protection Systems (PM-FPS).
The Remote Sensor Station (RSS) provides FIRRE with the ability to remote (or extend the range of) manned/unmanned sensors. The RSS consists of three primary components: (1) an actively cooled and hermetically sealed (NEMA-4X) electronics enclosure, (2) a 22' telescoping tower, (3) and the PM-MEP 531A 2KW GENSET. The current configuration supports a Digital Imaging Infrared (DII) DI-5000 thermal imaging system/visual imaging system (TIS/VIS), a Syracuse Research Corporation (SRC) PPS-5D ground surveillance radar (GSR), an AN/PRS-9 (BAIS) unmanned ground sensor (UGS) receiver, an Intuicom Military Navigator II (MILNAVII) data link radio, and a DTC Communications Palladium 12000 audio/video (A/V) radio. The electronics box is insulated with a radiant barrier and fitted with a EIC Solutions 1500 BTU solid state thermoelectric cooler (TEC) capable of maintaining a safe operating temperature in extreme conditions (<120° Fahrenheit).
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