Anomalous Driving Detection for Traffic Surveillance Video Analysis

Hang Shi, Hadi Ghahremannezhad, Chengjun Liu

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

4 Scopus citations

Abstract

Traffic safety is an important topic in the intelligent transportation system. One major factor that causes traffic accident is anomalous driving. This paper presents a novel anomalous driving detection method in videos, which can detect unsafe anomalous driving behaviors. The contributions of this paper are three-fold. First, a new multiple object tracking (MOT) method is proposed to extract the velocities and trajectories of moving foreground objects in video. The new MOT method is a motion based tracking method, which integrates the temporal and spatial features. Second, a novel Gaussian local velocity (GLV) modeling method is presented to model the normal moving behavior in traffic videos. The GLV model is built for every location in the video frame, and updated online. Third, a discrimination function is proposed to detect anomalous driving behaviors. Experimental results using the real traffic data from the New Jersey Department of Transportation (NJDOT) show that our proposed method can perform anomalous driving detection fast and accurately.

Original languageEnglish (US)
Title of host publicationIST 2021 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173719
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Imaging Systems and Techniques, IST 2021 - Virtual, New York, United States
Duration: Aug 24 2021Aug 26 2021

Publication series

NameIST 2021 - IEEE International Conference on Imaging Systems and Techniques, Proceedings

Conference

Conference2021 IEEE International Conference on Imaging Systems and Techniques, IST 2021
Country/TerritoryUnited States
CityVirtual, New York
Period8/24/218/26/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Decision Sciences (miscellaneous)

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