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


Conference Paper

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. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Chen, Q; Qiu, Q; Li, H; Wu, Q

Published Date

  • December 1, 2013

Published In

Start / End Page

  • 202 - 205

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781479910717

Digital Object Identifier (DOI)

  • 10.1109/ICCAD.2013.6691119

Citation Source

  • Scopus