Saliency Annotation of Image and Video Data

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-12-C-1469
Agency Tracking Number: F10B-T15-0232
Amount: $750,000.00
Phase: Phase II
Program: STTR
Awards Year: 2012
Solicitation Year: 2010
Solicitation Topic Code: AF10-BT15
Solicitation Number: 2010.B
Small Business Information
6800 Cortona Drive, Goleta, CA, -
DUNS: 054672662
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Kevin Sullivan
 Vice-President, Senior Scientist
 (805) 968-6787
Business Contact
 Marcella Lindbery
Title: Director of Contracts
Phone: (805) 968-6787
Research Institution
 The Pennsylvania State University
 Christine Wilson
 101 Hammond Building
Philadelphia, PA, 16802-
 (814) 865-3272
 Nonprofit college or university
ABSTRACT: Toyon Research Corporation and the Pennsylvania State University propose to develop a software tool which allows an operator to interactively identify suspicious activities. The tool will be called ALARM (Active Learning for Anomaly Recognition and Mensuration). It will ingest a database of track data and automatically cluster the data and develop statistical models of the tracks. The statistical models will be used to rank the tracks in terms of the most anomalous to the least anomalous. The operator will observe the most anomalous tracks and use contextual information to identify the most suspicious tracks. The ALARM inference algorithms will use the labels provided by the operator to classify the labeled tracks as suspicious and to identify unlabeled tracks that may or may not have been classified as anomalous, and present these tracks to the operator for further labeling and classification. The track database that we will use during Phase II will be derived using stored data collected by the ARGUS-IS sensor. We will improve and run automated detection and tracking algorithms on the ARGUS-IS data to create the tracks. BENEFIT: The successful completion of the proposed research will allow analysts to effectively sift through large volumes of video and track data in short periods of time. This will be made possible by making use of low-level automation for video processing and tracking combined with high-level reasoning by humans to recognize what is suspicious and what is not. A classifier that treats as inputs features derived from the track measurements will be actively learned both to automatically predict whether given tracks are suspicious or not and to rank all tracks for prioritized forwarding to a human analyst for labeling.

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

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