15,055 research outputs found
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
3DTouch: A wearable 3D input device with an optical sensor and a 9-DOF inertial measurement unit
We present 3DTouch, a novel 3D wearable input device worn on the fingertip
for 3D manipulation tasks. 3DTouch is designed to fill the missing gap of a 3D
input device that is self-contained, mobile, and universally working across
various 3D platforms. This paper presents a low-cost solution to designing and
implementing such a device. Our approach relies on relative positioning
technique using an optical laser sensor and a 9-DOF inertial measurement unit.
3DTouch is self-contained, and designed to universally work on various 3D
platforms. The device employs touch input for the benefits of passive haptic
feedback, and movement stability. On the other hand, with touch interaction,
3DTouch is conceptually less fatiguing to use over many hours than 3D spatial
input devices. We propose a set of 3D interaction techniques including
selection, translation, and rotation using 3DTouch. An evaluation also
demonstrates the device's tracking accuracy of 1.10 mm and 2.33 degrees for
subtle touch interaction in 3D space. Modular solutions like 3DTouch opens up a
whole new design space for interaction techniques to further develop on.Comment: 8 pages, 7 figure
Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification
Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.Advanced Research Projects Agency (ONR N00014-92-J-401J, ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530, IRI-90-24877, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100); British Petroleum (89-A-1204); Air Force Office of Scientific Research (F49620-92-J-0334
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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
Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment
In the last decade, integrated logistics has become an important challenge in
the development of wireless communication, identification and sensing
technology, due to the growing complexity of logistics processes and the
increasing demand for adapting systems to new requirements. The advancement of
wireless technology provides a wide range of options for the maritime container
terminals. Electronic devices employed in container terminals reduce the manual
effort, facilitating timely information flow and enhancing control and quality
of service and decision made. In this paper, we examine the technology that can
be used to support integration in harbor's logistics. In the literature, most
systems have been developed to address specific needs of particular harbors,
but a systematic study is missing. The purpose is to provide an overview to the
reader about which technology of integrated logistics can be implemented and
what remains to be addressed in the future
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti
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