Manchester Metropolitan University's Research Repository

Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

Zhang, Huaizhong, Liptrott, Mark, Bessis, Nik and Cheng, Jianquan ORCID logoORCID: https://orcid.org/0000-0001-9778-9009 (2019) Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 18 September 2019 - 21 September 2019, Taipei, Taiwan.


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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 pre-trained model used.

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