A Novel Methodology of Time Dependent Mean Field Based Multilayer Unsupervised Anomaly Detection Using Traffic Surveillance Videos

Fan Zuo, Jingqin Gao, Di Yang, Kaan Ozbay

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

Abstract

Real time anomaly detection has been attracting considerable interest in traffic control and management applications. As a low-cost, high-efficiency emerging technology, automated video analysis has been adopted by agencies that are convinced of the feasibility of this new technology due to the impressive developments in computer vision and image processing fields. This paper presents a two-layer learning framework for unsupervised anomaly detection based on traffic surveillance videos. The static layer uses background extraction and blurring of moving vehicles to identify static vehicles. The dynamic layer uses proposed time dependent vector field cross correlation method, processes the traffic flow into vector fields and, compares the difference between average vector fields and instant fields to determine anomaly events. This framework has been shown to have a low training cost, acceptable accuracy, and relatively high level of adaptability for real-life video sets obtained from different locations.

Original languageEnglish (US)
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-381
Number of pages6
ISBN (Electronic)9781538670248
DOIs
StatePublished - Oct 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: Oct 27 2019Oct 30 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
CountryNew Zealand
CityAuckland
Period10/27/1910/30/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management Science and Operations Research
  • Instrumentation
  • Transportation

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  • Cite this

    Zuo, F., Gao, J., Yang, D., & Ozbay, K. (2019). A Novel Methodology of Time Dependent Mean Field Based Multilayer Unsupervised Anomaly Detection Using Traffic Surveillance Videos. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 376-381). [8917034] (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917034