Divers executing strategic underwater missions as well as recreational divers have a great need for communication.
Divers can communicate between themselves as well as to a surface boat, to share information, perform cooperative
maneuvers and call for help. In addition, it would be useful to know the location of all divers relative to the boat. Such
capability will allow operators to guide divers in their maneuvers and provide immediate assistance during emergencies.
We present the development of communication protocols for text messaging and the development of a relative diver
Seismic Unattended Ground Sensors (UGS) are low cost and covert, making them a suitable candidate for border patrol.
Current seismic UGS systems use cadence-based intrusion detection algorithms and are easily confused between humans
and animals. The poor discrimination ability between humans and animals results in missed detections as well as higher
false (nuisance) alarm rates. In order for seismic UGS systems to be deployed successfully, new signal processing
algorithms with better discrimination ability between humans and animals are needed. We have characterized the
seismic signals using frequency domain and time-frequency domain statistics, which improve the discrimination
between humans, animals and vehicles.
With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence,
Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational
awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance
video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to
identify moving objects with a classification algorithm based on both static and kinematic features of the objects.
Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction
methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up
Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the
video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies
characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates
and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the
acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of
five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and
the object of interest.
In this paper, we address the problem of robust detection of dismounts from low-resolution video data sequences. We
outline a methodology based on SSCI's ultra-fast image alignment algorithm, and a combination of static and kinematic
features for dismount detection. We perform the dismount detection classification using a learning classifier algorithm.
Our results are promising and very valuable for low-resolution imagery where previous techniques for dismount
detection such as SURF and SIFT features do not perform very well.