5 research outputs found
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occupancy. Even though standard procedures are used by ATC, the control
of the aircraft remains with the pilots, leading to a large variability in the
flight patterns observed. Two methods to identify typical operations and their
variability from recorded radar tracks are presented. This knowledge base is
then used to monitor the conformance of current operations against operations
previously identified as standard. A tool called AirTrajectoryMiner is
presented, aiming at monitoring the instantaneous health of the airspace, in
real time. The airspace is "healthy" when all aircraft are flying according to
the nominal procedures. A measure of complexity is introduced, measuring the
conformance of current flight to nominal flight patterns. When an aircraft does
not conform, the complexity increases as more attention from ATC is required to
ensure a safe separation between aircraft.Comment: 15 pages, 20 figure
Real-Time Anomaly Detection in Full Motion Video
Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects within a video sequence and attempts to cluster each object\u27s trajectory into a database of existing trajectories. Objects are tracked by first differentiating them from a Gaussian background model and then tracked over subsequent frames based on a combination of size and color. Once an object is tracked over several frames, its trajectory is calculated and compared with other trajectories earlier in the video sequence. Anomalous trajectories are differentiated by their failure to cluster with other well-known movement patterns. Adding the proposed algorithm to an existing surveillance system could increase the likelihood of identifying an anomaly and allow for more efficient collection of intelligence data. Additionally, by operating in real-time, our algorithm allows for the reallocation of sensing equipment to those areas most likely to contain movement that is valuable for situational awareness
Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment
The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders
Mining Aircraft Telemetry Data With Evolutionary Algorithms
The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a
mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS)
operations developed by the University of North Dakota. GPAR-RMS detected proximate
aircraft with various sensor systems, including a 2D radar and an Automatic Dependent
Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then
displayed to UAS operators via visualization software developed by the University of
North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to
estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a
General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding
airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However,
accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR
in Class E airspace were needed before the RM subsystem could be implemented.
In this dissertation the author presents the results of data mining an aircraft
telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry
data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000
devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet.
Data from aircraft which were potentially within the controlled airspace surrounding
controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E
airspace were assumed to be flying under VFR, which is usually a valid assumption.
Complex subpaths were discovered from the aircraft telemetry data set using a novel
application of an ant colony algorithm. Then, probabilistic models were data mined from
those subpaths using extensions of the Genetic K-Means (GKA) and Expectation-
Maximization (EM) algorithms.
The results obtained from the subpath discovery and data mining suggest a pilot
flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than
a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of
the GA aircraft. However, since only aircraft telemetry data from the University of North
Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA
aircraft operating in a non-training environment
Detection of unusual fish trajectories from underwater videos
Fish behaviour analysis is a fundamental research area in marine ecology as it is helpful
for detecting environmental changes by observing unusual fish patterns or new fish
behaviours. The traditional way of analysing fish behaviour is by visual inspection
using human observers, which is very time consuming and also limits the amount of
data that can be processed. Therefore, there is a need for automatic algorithms to identify
fish behaviours by using computer vision and machine learning techniques. The
aim of this thesis is to help marine biologists with their work. We focus on behaviour
understanding and analysis of detected and tracked fish with unusual behaviour detection
approaches. Normal fish trajectories exhibit frequently observed behaviours while
unusual trajectories are outliers or rare trajectories.
This thesis proposes 3 approaches to detecting unusual trajectories: i) a filtering
mechanism for normal fish trajectories, ii) an unusual fish trajectory classification
method using clustered and labelled data and iii) an unusual fish trajectory classification
approach using a clustering based hierarchical decomposition.
The rule based trajectory filtering mechanism is proposed to remove normal fish
trajectories which potentially helps to increase the accuracy of the unusual fish behaviour
detection system. The aim is to reject normal fish trajectories as much as possible
while not rejecting unusual fish trajectories. The results show that this method
successfully filters out normal trajectories with a low false negative rate. This method
is useful to assist building a ground truth data set from a very large fish trajectory
repository, especially when the amount of normal fish trajectories greatly dominates
the unusual fish trajectories. Moreover, it successfully distinguishes true fish trajectories
from false fish trajectories which result from errors by the fish detection and
tracking algorithms.
A key contribution of this thesis is the proposed flat classifier, which uses an outlier
detection method based on cluster cardinalities and a distance function to detect unusual
fish trajectories. Clustered and labelled data are used to select feature sets which
perform best on a training set. To describe fish trajectories 10 groups of trajectory
descriptions are proposed which were not previously used for fish behaviour analysis.
The proposed flat classifier improved the performance of unusual fish detection
compared to the filtering approach.
The performance of the flat classifier is further improved by integrating it into a
hierarchical decomposition. This hierarchical decomposition method selects more specific
features for different trajectory clusters which is useful considering the trajectory
variety. Significantly improved results were obtained using this hierarchical decomposition
in comparison to the flat classifier. This hierarchical framework is also applied
to classification of more general imbalanced data sets which is a key current topic in
machine learning. The experiments showed that the proposed hierarchical decomposition
method is significantly better than the state of art classification methods, other
outlier detection methods and unusual trajectory detection methods. Furthermore, it is
successful at classifying imbalanced data sets even though the majority and minority
classes contain varieties, and classes overlap which is frequently seen in real-world
applications.
Finally, we explored the benefits of active learning in the context of the hierarchical
decomposition method, where active learning query strategies choose the most
informative training data. A substantial performance gain is possible by using less labelled
training data compared to learning from larger labelled data sets. Additionally,
active learning with feature selection is investigated. The results show that feature selection
has a positive effect on the performance of active learning. However, we show
that random selection can be as effective as popular active learning query strategies in
combination with active learning and feature selection, especially for imbalanced set
classification