Multiple Views Tracking of Maritime Targets

This paper explores techniques for multiple views target tracking in a maritime environment using a mobile surveillance platform. We utilise an omnidirectional camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to estimate the platform?s ego-motion. For each target a part of the omnidirectional video is extracted, forming a corresponding set of virtual cameras. Each target is then tracked using a dynamic template matching method and particle filtering. Its predictions are then used to continuously adjust the orientations of the virtual cameras, keeping a lock on the targets. We demonstrate the performance of the application in several real-world maritime settings

First Measurements with NeXtRAD, a Polarimetric X/L Band Radar Network

NeXtRAD is a fully polarimetric, X/L Band radar network. It is a development of the older NetRAD system and builds on the experience gained with extensive deployments of NetRAD for sea clutter and target measurements. In this paper we will report on the first measurements with NeXtRAD, looking primarily at sea clutter and some targets, as well as early attempts at calibration using corner reflectors, and an assessment of the polarimetric response of the system. We also highlight innovations allowing for efficient data manipulation post measurement campaigns, as well as the plans for the coming years with this system

A vision system for mobile maritime surveillance platforms

Mobile surveillance systems play an important role to minimise security and safety threats in high-risk or hazardous environments. Providing a mobile marine surveillance platform with situational awareness of its environment is important for mission success. An essential part of situational awareness is the ability to detect and subsequently track potential target objects.Typically, the exact type of target objects is unknown, hence detection is addressed as a problem of finding parts of an image that stand out in relation to their surrounding regions or are atypical to the domain. Contrary to existing saliency methods, this thesis proposes the use of a domain specific visual attention approach for detecting potential regions of interest in maritime imagery. For this, low-level features that are indicative of maritime targets are identified. These features are then evaluated with respect to their local, regional, and global significance. Together with a domain specific background segmentation technique, the features are combined in a Bayesian classifier to direct visual attention to potential target objects.The maritime environment introduces challenges to the camera system: gusts, wind, swell, or waves can cause the platform to move drastically and unpredictably. Pan-tilt-zoom cameras that are often utilised for surveillance tasks can adjusting their orientation to provide a stable view onto the target. However, in rough maritime environments this requires high-speed and precise inputs. In contrast, omnidirectional cameras provide a full spherical view, which allows the acquisition and tracking of multiple targets at the same time. However, the target itself only occupies a small fraction of the overall view. This thesis proposes a novel, target-centric approach for image stabilisation. A virtual camera is extracted from the omnidirectional view for each target and is adjusted based on the measurements of an inertial measurement unit and an image feature tracker. The combination of these two techniques in a probabilistic framework allows for stabilisation of rotational and translational ego-motion. Furthermore, it has the specific advantage of being robust to loosely calibrated and synchronised hardware since the fusion of tracking and stabilisation means that tracking uncertainty can be used to compensate for errors in calibration and synchronisation. This then completely eliminates the need for tedious calibration phases and the adverse effects of assembly slippage over time.Finally, this thesis combines the visual attention and omnidirectional stabilisation frameworks and proposes a multi view tracking system that is capable of detecting potential target objects in the maritime domain. Although the visual attention framework performed well on the benchmark datasets, the evaluation on real-world maritime imagery produced a high number of false positives. An investigation reveals that the problem is that benchmark data sets are unconsciously being influenced by human shot selection, which greatly simplifies the problem of visual attention. Despite the number of false positives, the tracking approach itself is robust even if a high number of false positives are tracked

On Small Satellites for Oceanography: A Survey

The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Department of Homeland Security Science and Technology Directorate: Developing Technology to Protect America

In response to a congressional mandate and in consultation with Department of Homeland Security's (DHS) Science and Technology Directorate (S&T), the National Academy conducted a review of S&T's effectiveness and efficiency in addressing homeland security needs. This review included a particular focus that identified any unnecessary duplication of effort, and opportunity costs arising from an emphasis on homeland security-related research. Under the direction of the National Academy Panel, the study team reviewed a wide variety of documents related to S&T and homeland security-related research in general. The team also conducted interviews with more than 200 individuals, including S&T officials and staff, officials from other DHS component agencies, other federal agencies engaged in homeland security-related research, and experts from outside government in science policy, homeland security-related research and other scientific fields.Key FindingsThe results of this effort indicated that S&T faces a significant challenge in marshaling the resources of multiple federal agencies to work together to develop a homeland security-related strategic plan for all agencies. Yet the importance of this role should not be underestimated. The very process of working across agencies to develop and align the federal homeland security research enterprise around a forward-focused plan is critical to ensuring that future efforts support a common vision and goals, and that the metrics by which to measure national progress, and make changes as needed, are in place

Contextual Knowledge and Information Fusion for Maritime Piracy Surveillance

Proceedings of: NATO Advanced Study Institute (ASI) on Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, Salamanca, 19-30 September, 2011Though piracy accounts for only a small fraction of the general losses of the maritime industry it creates a serious threat to the maritime security because of the connections between organized piracy and wider criminal networks and corruption on land. Fighting piracy requires monitoring the waterways, harbors,and criminal networks on the land to increase the ability of the decision makers to predict piracy attracts and manage operations to prevent or contain them. Piracy surveillance involves representing and processing huge amount heterogeneous information often uncertain, unreliable, and irrelevant within a specific context to detect and recognize suspicious activities to alert decision makers on vessel behaviors of interest with minimal false alarm. The paper discusses the role of information fusion, and context representation and utilization in building an piracy surveillance picture.This paper has utilized the results of the research activity supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC and CAM CONTEXTS (S2009/TIC-1485)Publicad

Establishing a Multibeam Sonar Evaluation Test Bed near Sidney, British Columbia

The Canadian Hydrographic Service (CHS), Naval Oceanographic Office (NAVOCEANO) and the Ocean Mapping Group of the University of New Brunswick (OMG) collaborated on establishing a multibeam sonar test bed in the vicinity of the Institute of Ocean Sciences in Sidney, British Columbia Canada. This paper describes the purpose of the sonar evaluation test bed, the trials and tribulations of two foreign governments collaborating on projects of mutual interest, the evaluation areas and their characteristics for sonar testing, and sample results of sonar evaluations using this test bed. Some target detection comparisons of several systems over a range of artificial sonar targets will also be given