Embedding of Advanced Search Technique for Detect, Locate, and Track for Pedestrian-based Search

Award Information
Agency:
Department of Homeland Security
Branch
n/a
Amount:
$149,942.65
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
HSHQDC-12-C-00108
Award Id:
n/a
Agency Tracking Number:
DNDOSBIR12-02-FP-001-IOS
Solicitation Year:
2012
Solicitation Topic Code:
12.1-002
Solicitation Number:
n/a
Small Business Information
CA, Torrance, CA, 90505-5217
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
033449757
Principal Investigator:
Andy Gooden
sbirproposals@intopsys.com
Business Contact:
Reuben Sandler
randdoffice@intopsys.com
Research Institution:
n/a
Abstract
Techniques employed by expert operators to enhance the ability to detect, locate and track radiation sources are straightforward in concept but require substantial skill in execution and in interpretation of the detector readings obtained, combined with the operator's visual observations. By combining detectors with fast acquisition times, below 500 milliseconds, with the video and computational capabilities contained in low cost COTS systems such as smart phones, tablets, or other embedded processors, algorithms can be developed that enhance detector components with improved capabilities and make these expert search techniques even more effective. Additionally, although not a primary goal, these expert techniques will be accessible to a much larger population of operators and could reduce training costs. Enabling an automated system to duplicate the performance of an expert operator is a sensor data fusion problem. The automated system must acquire data from a set of sensors and combine the data in a manner that at least matches the effectiveness of the expert operator. For the current problem this means fusing location, bearing, video (visual), motion and radiation data. Of these, video sensors present a unique challenge because of the very large and noisy data they present, requiring the use of sophisticated feature extraction techniques. Intelligent Optical Systems' approach will leverage the increased computing power now available on COTS platforms as well as existing software resources such as OpenCV or Kinect for Windows for video processing to address this challenge. The data provided by other sensors, including accelerometers, magnetometers and GPS can be converted to a useful form by relatively straightforward software operation. After extraction of video features the input data, including video, bearing, motion, location and radiation will be combined using a particle filter implementation of a dynamic Bayesian network. This has been shown to be an effective technique for fusing various types of input data, specifically including radiation and location data. We will innovate to extend the algorithms to include extracted video, bearing and motion data. Through this program we will produce a set of products that provide a significant enhancement to the performance of COTS detectors at low cost. Selected algorithms, applications, visualization and computational capabilities of the system will be implemented in a breadboard prototype at Technology Readiness Level TRL 4 to TRL 5, and investigated in Phase I. Based on these results in Phase II the research and development effort will be both broadened, to encompass additional COTS components, and deepened to more fully investigate the optimum methods of enhancing performance, reaching TRL 8 at the conclusion of Phase II. In total the products developed under this SBIR will provide an extremely cost effective method to enhance the capabilities of existing radiation detectors. Intelligent Optical Systems’ team for this investigation has unique recent experience with this subject matter and a broad background in algorithms and techniques for detection, location and tracking of radiation sources as well as networking protocols, handheld electronics, and embedded software.

* information listed above is at the time of submission.

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