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NCJ Number: 250273 Find in a Library
Title: Learning Models for Predictive Behavioral Intent and Activity Analysis in Wide Area Video Surveillance
Author(s): Shishir K. Shah
Date Published: October 2016
Page Count: 16
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 2009-MU-MU-K004
Sale Source: US Dept of Justice NIJ Pub
810 Seventh Street, NW
Washington, DC 20531
United States of America
Document: PDF
Type: Grants and Funding; Report (Grant Sponsored); Report (Study/Research); Research (Applied/Empirical)
Format: Document; Document (Online)
Language: English
Country: United States of America
Annotation: Methodology and outcomes are reported for a research project that focused on the development of new methods and algorithms for wide-area surveillance and understanding of human activities in large distributed camera networks.
Abstract: The project’s main product was research publications that span algorithms for tracking individual and group activity and the re-identification of individuals and groups. Each of the algorithms was developed and tested on public and small benchmark datasets. These are stand-alone evaluations that have not yet been integrated into a system solution. A prototype code was developed for person re-identification that facilitates production of a gallery probe from input videos and performs matching of an input observation to the identifications in the gallery. Although this is not an integrated system for re-identification, the developed prototype has two modular components. One module takes in a video and performs human detection and tracking. Tracking allows for the production of either a gallery or probe dataset. The second module includes human parts-based re-identification. The images in the specified gallery and probe datasets are initially segmented to identify body parts. An appearance descriptor is produced as a model for each body part and integrated over multiple images of the person, if available. This technology provides automatic monitoring of surveillance videos, which helps security officers detect and predict suspicious activities; this facilitates the prevention or mitigation of a security threat. The ability to model simple activity patterns from a single camera’s video data has been demonstrated for a variety of applications. The challenges that remain are related to person re-identification and the understanding of human behavior. The latter is related to an overall lack of understanding of human behavior, both as individuals and groups. 4 figures and 4 references
Main Term(s): Technology transfer
Index Term(s): Behavior patterns; Behavior typologies; Closed circuit television (CCTV); Crime prediction; Criminality prediction; National Institute of Justice (NIJ); NIJ final report; NIJ Resources; Suspect identification
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