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A new paradigm for persistent wide area surveillance

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7 Author(s)
Coulter, L.L. ; Dept. of Geogr., San Diego State Univ., San Diego, CA, USA ; Stow, D.A. ; Yu Hsin Tsai ; Chavis, C.M.
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A novel and patent pending approach representing a new paradigm for persistent wide area surveillance is presented. As part of the Department of Homeland Security (DHS) National Center for Border Security and Immigration (BORDERS), San Diego State University (SDSU) researchers have developed a method for accurate and automated detection of people and vehicles moving through remote border regions or other uninhabited areas. Instead of imaging small areas at video frame rates (as with traditional surveillance), the approach uses a repeat pass, location-based image capture approach that trades time for space and enables repetitive imaging of large areas at lower repetition rates. Multiple targets may be detected and tracked over large areas, compared to video monitoring which focuses on small areas and often on individual targets previously detected using other means. The approach utilizes high frequency, repeat pass image collection (e.g., same imaging stations every 15 minutes) with frame array cameras to monitor large areas with a single aircraft and detect and locate objects moving through uninhabited landscapes. High frequency imaging from low-cost light aircraft is utilized to characterize the expected brightness response of each patch of ground corresponding to the ground resolution element of a pixel (3-inch ground sampling distance for this study), and an anomaly detection algorithm is used to detect subtle deviations from this expected response. Once anomalies (i.e., objects that have moved) are detected, small image chips (i.e., subsets) may be transmitted wirelessly to command and control stations so that the detection results may be visually verified in near real-time. The detection algorithm utilizes unique change detection thresholds per pixel, making the approach highly sensitive. Initial test results indicate that 98% of people and 100% of vehicles were correctly detected, with virtually no false detection (only 12 pixels within 19 images 21 megapixel- in size). SDSU researchers, NEOS Ltd., and commercial partners are working to build a prototype system to further test and demonstrate this near real-time detection approach. This work is developed by the National Center for Border Security and Immigration: A Department of Homeland Security Science and Technology Center of Excellence.

Published in:

Homeland Security (HST), 2012 IEEE Conference on Technologies for

Date of Conference:

13-15 Nov. 2012