Scene categorization towards urban tunnel traffic by image quality assessment

Scene categorization is an indispensable technique in intelligent systems, such as scene parsing, video surveillance or autonomous driving. Considering traffic analysis under big data, in this paper, we propose scene categorization towards urban tunnel traffic based on image quality assessment. Specifically, the dataset is obtained through analyzing urban tunnel traffic incidents from 2016 to 2018. And we classify the traffic accidents in the big data environment. Then, the vehicles in the surveillance videos are extracted using conventional detector. The spatial information of vehicles in the image reflects the traffic situation. In order to encode such important information, we leverage the information clustering algorithm based on information entropy for image classification. Afterward, we establish a quality evaluation model based on each clustered images. The trained image quality assessment model will guide tunnel traffic classification and event analysis. The experimental results show the correct rate is more than 90%, and the overall detection effect is better than the k-modes algorithm and the Ng’k-modes algorithm.