10,025 research outputs found
Fish tracking technology development. Phases 1 and 2, project definition desk study and equipment
The document reports on the major findings from a definition study to appraise the options to develop fish tracking equipment, in particular tags and data logging systems, in order to improve the effeciency of the Agency tracking studies and to obtain a greater understanding of fish biology. The definition study was in two parts. The first, Phase 1, collated and evaluated all the known tracking systems that may be suitable for studies of fish which are either produced commercially or have been constructed for specific in-house studies. Phase 2 was an evaluation of all the tracking equipment considered to merit further investigation in Phase 1. The deficiencies between existing and required technologies to improve the efficiency of Agency's tracking studies and to obtain a greater understanding of fish biology are also identified
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
The Rise of Certificate Transparency and Its Implications on the Internet Ecosystem
In this paper, we analyze the evolution of Certificate Transparency (CT) over
time and explore the implications of exposing certificate DNS names from the
perspective of security and privacy. We find that certificates in CT logs have
seen exponential growth. Website support for CT has also constantly increased,
with now 33% of established connections supporting CT. With the increasing
deployment of CT, there are also concerns of information leakage due to all
certificates being visible in CT logs. To understand this threat, we introduce
a CT honeypot and show that data from CT logs is being used to identify targets
for scanning campaigns only minutes after certificate issuance. We present and
evaluate a methodology to learn and validate new subdomains from the vast
number of domains extracted from CT logged certificates.Comment: To be published at ACM IMC 201
Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery
As part of a multibeam and side scan sonar (SSS) benthic survey of the Marine Conservation District (MCD) south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank (LB) for red hind (Epinephelus guttatus) and the Mutton Snapper (MS) (Lutjanus analis) area—we extracted signals from water column targets that represent individual
and aggregated fish over various benthic habitats encountered in the SSS imagery. The survey covered a total of 18 km2 throughout the federal jurisdiction fishery management areas. The complementary set of 28 habitat classification digital maps covered a total of 5,462.3 ha;
MCDW (West) accounted for 45% of that area, and MCDE (East) 26%, LB 17%, and MS the remaining 13%. With the exception
of MS, corals and gorgonians on consolidated habitats were significantly more abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments. Continuous coral habitat was the most abundant consolidated habitat for both MCDW and MCDE (41% and 43% respectively). Consolidated habitats in LB and MS predominantly consisted of gorgonian plain habitat with 95% and 83% respectively. Coral limestone habitat was more abundant than coral patch habitat; it was found near the shelf break in MS, MCDW, and MCDE. Coral limestone and coral patch habitats only covered LB minimally. The high spatial resolution (0.15 m) of the acquired imagery allowed the detection of differing fish aggregation (FA) types. The
largest FA densities were located at MCDW and MCDE over coral communities that occupy up to 70% of the bottom cover.
Counts of unidentified swimming objects (USOs), likely representing individual fish, were similar among locations and occurred primarily over sand and shelf edge areas. Fish aggregation school sizes were significantly smaller at MS than the other three locations (MCDW, MCDE, and LB). This study shows the advantages of utilizing SSS in determining fish distributions and density
Fish tracking technology development. Phase 1 project definition desk study
The document reports on Phase 1 of a definition study to appraise the options to develop fish tracking equipment, in particular tags and data logging systems in order to improve the efficiency of the Environment Agency's tracking studies and to obtain a greater understanding of fish
biology.
Covered in this report are radio telemetry, audio telemetry, High Resolution Position Fixing, data storage and archival tags and other fish tracking systems such as biosonics
Region-enhanced passive radar imaging
The authors adapt and apply a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations that are reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Owing to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artefacts in the image, as compared with the case of active SAR imaging. The regionenhanced image formation method considered is based on an explicit mathematical model of the observation process; hence, information about the nature of the aperture is explicitly taken into account in image formation. Furthermore, this framework allows the incorporation of prior information or constraints about the scene being imaged, which makes it possible to compensate for the limitations of the sparse apertures involved in passive radar imaging. As a result, conventional imaging artefacts, such as sidelobes, can be alleviated. Experimental results using data based on electromagnetic simulations demonstrate that this is a promising strategy for passive radar imaging, exhibiting significant suppression of artefacts, preservation of imaged object features, and robustness to measurement noise
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