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NCJ Number: NCJ 235579     Find in a Library
Title: Automated Detection and Prevention of Disorderly and Criminal Activities
Author(s): Nils Krahnstoever
Date Published: 08/2011
Page Count: 125
Sponsoring Agency: National Institute of Justice
US Department of Justice
Office of Justice Programs
United States of America
Grant Number: 2007-RG-CX-K015
Sale Source: National Institute of Justice/NCJRS
Box 6000
Rockville, MD 20849
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF 
Type: Report (Study/Research)
Language: English
Country: United States of America
Annotation: This report describes the development of a wide range of intelligent video capabilities relevant to law enforcement and corrections that can detect activities and alert operators allowing for early detection and prevention.
Abstract: The technology is intended to go beyond simple motion-based behavioral features to a semantically high level of understanding human activities by estimating meaningful social relationships between people and groups who are interacting in crowded environments. One of the main technical challenges was to detect events as well as motion and behavior patterns from tracks of people in crowded environments. The technology to be developed was required to recognize common group and crowd motion patterns, along with parameters such as crowd size, crowd velocity, agitation level, and events such as group formation and dispersion. The detection and recognition must occur on noisy data obtained by a video tracking system. A second technological challenge was to establish identity records of individuals based on their facial images in non-cooperative environments. Once identity records are established, associations (interactions) between subjects can be recorded; over time, association graphs can be built that represent the social connections between individuals. An overview is provided of the various motion and behavior pattern recognitions as well as the facial capture and social network estimation algorithms developed under this program. A performance evaluation was conducted based on the data collected at the 2009 Mock Prison Riot. The analysis indicates that the current system has an approximately 70-percent chance of detecting the occurrence of disorderly or aggressive events in the observed prison environment and currently has a 20-percent chance of predicting the event before it occurs. Examples are provided of how the technology developed can impact law enforcement operations. 62 figures and 41 references
Main Term(s): Police equipment
Index Term(s): Civil disorders ; Disorderly conduct ; Crime detection ; Prison disorders ; Technology transfer ; Science and Technology ; Visual electronic surveillance ; Crime prevention planning ; Video imaging
   
  To cite this abstract, use the following link:
https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=257560

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