Title: An automatic traffic-congestion detection method for bad weather based on traffic video

Authors: Jieren Cheng; Boyi Liu; Xiangyan Tang

Addresses: Hainan University, 570228, Haikou, China; State Key Laboratory of Marine Resource Utilization in South China Sea, 570228, Haikou, China ' Hainan University, 570228, Haikou, China ' Hainan University, 570228, Haikou, China

Abstract: The result of automatic traffic-congestion detection method in bad weather is inaccurate. In response to this situation, we propose a detection method of traffic congestion based on histogram equalisation and discrete-frame difference. This method uses discrete-frame difference algorithm to extract the images that have vehicle information firstly. Then, the method employs histogram equalisation algorithm to eliminate the noise of the vehicle images. We also propose a corresponding traffic congestion index statistical algorithm in this step. After that, this method recognises vehicles from the video and computes the traffic congestion index. Finally, the method transforms the dimension of traffic congestion index and obtains the state of traffic congestion. It is proved by experiments that the method increases the accuracy rate of automatic traffic congestion detection in bad weather. This method has a wonderful prospect in some areas where there is usually bad weather.

Keywords: traffic congestion; histogram equalisation; video processing; inter-frame difference.

DOI: 10.1504/IJHPCN.2018.091909

International Journal of High Performance Computing and Networking, 2018 Vol.11 No.3, pp.251 - 259

Available online: 10 May 2018 *

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