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Video Analysis for Nighttime Surveillance and Situational Awareness

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: DAAH0103CR279
Agency Tracking Number: 03ST1-003
Amount: $98,915.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2003
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mark Stevens
 Senior Scientist
 (617) 491-3474
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Research Institution
 Steven Singer
Office of Sponsored Programs, 25 Buick Street
Boston, MA 02215
United States

 (617) 353-4365
 Nonprofit College or University

Interpretation of video imagery is the quintessential goal of computer vision. The ability to group moving pixels into regions and then associate semantic labels with those regions has long been studied by the vision community. Only recently have thecomponent technologies matured sufficiently to make this goal attainable for well-defined scenarios. We propose a system for semantic interpretation of certain human behaviors in a nighttime parking lot surveillance scenario. The video stream is firstsegmented into moving objects of interest (people, cars) vs. background (ground, sky, buildings, trees, moving foliage, shadows, etc.). Trajectory analysis is then performed on each object, using robust feature tracking and 3D reconstruction. In parallel,body pose analysis is performed by a probabilistic framework that learns mappings from body silhouettes to physical pose. Trajectory and pose information is combined by inferencing over an iconic action grammar. Our approach to specifying interestingactions via behavioral models is novel, as is the identification of unauthorized actions based on top-down inferencing from these models. We will demonstrate using nighttime parking lot imagery from commercial off-the-shelf low-light video equipment. Toincrease robustness of event detection, we will also explore audio information of events such as cars starting. The proposed system and developed techniques are applicable to a wide range of DoD mission and intelligence community areas, including targetrecognition, HumanID, and surveillance & tracking. The technologies developed here could be used to model the behavior of terrorists and identify individuals engaging in illegal acts given a video stream of their motion through the environment. Thistechnology is directly applicable to various current DARPA programs (including Combat Zones that See).

* Information listed above is at the time of submission. *

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