A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring

Qiuwen Chen, Qinru Qiu, Hai Li, Qing Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.

Original languageEnglish (US)
Title of host publication2013 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013 - Digest of Technical Papers
Pages202-205
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 32nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013 - San Jose, CA, United States
Duration: Nov 18 2013Nov 21 2013

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Other

Other2013 32nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2013
CountryUnited States
CitySan Jose, CA
Period11/18/1311/21/13

Keywords

  • anomaly detection
  • cogent confabulation
  • neuromorphic architecture

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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