SBIR-STTR Award

A Semantic Analysis Based Automatic Object Classification and Activity Perception System for Large-view Urban Environments
Award last edited on: 7/8/2010

Sponsored Program
SBIR
Awarding Agency
DOD : DARPA
Total Award Amount
$98,999
Award Phase
1
Solicitation Topic Code
SB082-021
Principal Investigator
Genshe Chen

Company Information

DCM Research Resources LLC

14163 Furlong Way
Germantown, MD 20874
   (301) 528-4634
   N/A
   www.dcmresearchresources.com
Location: Single
Congr. District: 06
County: Montgomery

Phase I

Contract Number: ----------
Start Date: ----    Completed: ----
Phase I year
2009
Phase I Amount
$98,999
Currently available methods for object recognition and classification primarily rely on static information in single-frame images. However, for the combat aerial video (usually low resolution video), all the static indexes used for object classification and recognition are almost impossible to obtain. To address this challenge, researchers are particularly interested in developing new techniques to determine the type, functionality, purpose or intent of static and moving objects through analysis of dynamic information in persistent surveillance video. The objects of interest include buildings, functional areas, vehicles, and human beings. In this proposal, DCM Research Resources LLC and its collaborator propose to develop an automatic innovative 3D and dynamic semantic scene analysis based approach that exploits surveillance video data mainly captured from UAV platforms to classify buildings, vehicles, and people automatically. In our proposed automatic object detection and classification system, in addition to 3D static object’s visual features (e.g. building’s shape, line orientation, color, and texture) and the 3D static structures of the urban environment, we will also explore dynamic video features which include vehicle/human motion patterns over time. All these static and dynamic features will be used to construct spatial-temporal feature vectors, and the new generated vectors will then be sent to a probabilistic Dynamic Influence Diagram (DID) reasoning model for real-time and automatic building/vehicle classification. Besides the novel algorithms on building and vehicle classification, we also propose a Decentralized Dynamic Markov Game Model (DDMGM) for human activity and intent inference. In addition, we propose to develop novel 3D algorithms on automatic building detection, 3D terrain modeling, and visualization to support accurate object category and classification.

Keywords:
Video Analysis, Automatic Object Classification, Object Activity Perception, Semantic Analysis, Dynamic Influence Diagram, Dynamic Markov Game Model, 3d Terrain Modeling And R

Phase II

Contract Number: ----------
Start Date: ----    Completed: ----
Phase II year
----
Phase II Amount
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