Real-time Traffic Analysis Using Deep Learning Techniques And UAV Based Video

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Abstract

In urban environments there are daily issues of traffic
congestion which city authorities need to address. Realtime
analysis of traffic flow information is crucial for
efficiently managing urban traffic. This paper aims to
conduct traffic analysis using UAV-based videos and deep
learning techniques. The road traffic video is collected by
using a position-fixed UAV. The most recent deep learning
methods are applied to identify the moving objects in
videos. The relevant mobility metrics are calculated to
conduct traffic analysis and measure the consequences of
traffic congestion. The proposed approach is validated with
the manual analysis results and the visualization results.
The traffic analysis process is real-time in terms of the pretrained
model used.
Original languageEnglish
Number of pages5
Journal2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Early online date25 Nov 2019
DOIs
Publication statusE-pub ahead of print - 25 Nov 2019
Event16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - Taipei, Taiwan, Province of China
Duration: 18 Sep 201921 Sep 2019

Keywords

  • Traffic congestion
  • UAV
  • Deep learning

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