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Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage

Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia
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Infrastructures 2019, 4(2), 22; https://doi.org/10.3390/infrastructures4020022
Received: 6 April 2019 / Revised: 1 May 2019 / Accepted: 5 May 2019 / Published: 8 May 2019
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Abstract

Real-time traffic monitoring is essential in many novel applications, from traffic management to smart navigation systems. The large number of traffic cameras being integrated into urban infrastructures has enabled efficient traffic monitoring as an intervention in reducing traffic accidents and related casualties. In this paper, we focus on the problem of the automatic detection of anomalous driving behaviors, e.g., speeding or stopping on a bike lane, by using the traffic-camera feed that is available online. This can play an important role in personalized route-planning applications where, for instance, a user wants find the safest paths to get to a destination. We present an integrated system that accurately detects, tracks, and classifies vehicles using online traffic-camera feed. View Full-Text
Keywords: object detection; vehicle tracking; driving-behavior classification object detection; vehicle tracking; driving-behavior classification
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Zheng, X.; Wu, F.; Chen, W.; Naghizade, E.; Khoshelham, K. Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage. Infrastructures 2019, 4, 22.

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