We present a near-real-time visual-processing approach for automatic airborne target detection and classification. Detection is based on fast and robust background modeling and shape extraction, while recognition of target classes is based on shape and texture-fused querying on a-priori built real datasets. The presented approach can be used in defense and surveillance scenarios where passive detection capabilities are preferred (or required) over a secured area or protected zone.
In the past decades, many new trends appeared in interventional medicine. One of the most groundbreaking ones is
Image-Guided Surgery (IGS). The main benefit of IGS procedures is the reduction of the patient's pain and collateral
damage through improved accuracy and targeting. Electromagnetic Tracking (EMT) has been introduced to medical
applications as an effective tool for navigation. However, magnetic fields can be severely distorted by ferromagnetic
materials and electronic equipment, which is a major barrier towards their wider application. The focus of the study
is to determine and compensate the inherent errors of the different types of EMTs, in order to improve their accuracy.
Our aim is to develop a standardized, simple and repeatable assessment protocol; to determine tracking errors with
sub-millimeter accuracy, hence increasing the measurement precision and reliability. For initial experiments, the
NDI Aurora and the Ascension medSAFE systems were used in a standard laboratory environment. We aim to
advance to the state-of-the art by describing and disseminating an easily reproducible calibration method, publishing
the CAD files of the accuracy phantom and the source of the evaluation data. This should allow the wider spread of
the technique, and eventually lead to the repeatable and comparable assessment of EMT systems.
This paper presents visual detection and recognition of flying targets (e.g. planes, missiles) based on automatically extracted
shape and object texture information, for application areas like alerting, recognition and tracking. Targets are
extracted based on robust background modeling and a novel contour extraction approach, and object recognition is done
by comparisons to shape and texture based query results on a previously gathered real life object dataset. Application areas
involve passive defense scenarios, including automatic object detection and tracking with cheap commodity hardware
components (CPU, camera and GPS).
This paper presents methods and algorithms for real-time visual target detection, recognition and tracking, both in the case
of ground-based objects (surveyed from a moving airborne imaging sensor) and flying targets (observed from a ground-based
or vehicle mounted sensor). The methods are highly parallelized and partially implemented on GPU, with the goal
of real-time speeds even in the case of multiple target observations. Real-time applicability is in focus. The methods use
single camera observations, providing a passive and expendable alternative for expensive and/or active sensors. Use cases
involve perimeter defense and surveillance situations, where passive detection and observation is a priority (e.g. aerial
surveillance of a compound, detection of reconnaissance drones, etc.).
This paper presents an automatic approach for camera/image based detection, recognition and tracking of flying objects
(planes, missiles, etc.). The method detects appearing objects, and recognizes re-appearing targets. It uses a feature-based
statistical modeling approach (e.g. HMM) for motion-based recognition, and an image feature (e.g. shape) based indexed
database of pre-trained object classes, suitable for recognition on known and alerting on unknown objects. The method can
be used for detection of flying objects, recognition of the same object category through multiple views/cameras and signal
on unusual motions and shape appearances.
The paper presents an automatic approach for small moving object detection, categorization and unusual motion pattern
signaling on camera feeds on sky background. The method uses a local blind deconvolution based foreground detector for
small object mask and contour edge extraction, spatio-temporal localized histogram evaluation for object classification, and
a hidden Markov model based evaluation for learning usual motions and signaling unusual motion patterns. The method is
able to mask moving objects, fit them into learned categories and signal unexpected motion behavior.
A method is presented where the foreground is enhanced by an relative focus-map estimation. In the case of moving patterns a Stauffer Grimson approach is used to suppress background. In case of small and subpixel target size statistical evaluation is used for a rough classification. A new solution is given by a combination of foreground separation, relative focus-map generation and
histogram-based local evaluation to find the foreground objects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.