Scene categorization towards urban tunnel traffic by image quality assessment

Abstract 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.

[1]  Binbin Hao,et al.  Hybrid variational model based on alternating direction method for image restoration , 2019, Advances in Difference Equations.

[2]  Xiao Lu,et al.  Novel Model for Cascading Failure Based on Degree Strength and Its Application in Directed Gene Logic Networks , 2018, Comput. Math. Methods Medicine.

[3]  Zhang Jian-jun.  Traffic incident detection system based on video image processing , 2008 .

[4]  Hanxin Chen,et al.  Multiple fault condition recognition of gearbox with sequential hypothesis test , 2013 .

[5]  Yasuo Asakura,et al.  Incident Detection Methods Using Probe Vehicles with On-board GPS Equipment , 2015 .

[6]  Kai Li,et al.  Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization , 2019, Journal of Applied Remote Sensing.

[7]  Lin Zhiting,et al.  Freeway incident detection based on set theory and short-range communication , 2019 .

[8]  Xiulai Li,et al.  Research on iris image encryption based on deep learning , 2018, EURASIP J. Image Video Process..

[9]  Asdrubal Garcia-Ortiz,et al.  Traffic incident detection: Sensors and algorithms , 1998 .

[10]  P. Shan,et al.  Numerical Simulation of the Fluid–Solid Coupling Process During the Failure of a Fractured Coal–Rock Mass Based on the Regional Geostress , 2018, Transport in Porous Media.

[11]  Lei Lin.  Data science application in intelligent transportation systems: An integrative approach for border delay prediction and traffic accident analysis , 2015 .

[12]  Jaouad Boumhidi,et al.  Fuzzy deep learning based urban traffic incident detection , 2017, Cognitive Systems Research.

[13]  H. X. Chen,et al.  Semiconductivities of passive films formed on stainless steel bend under erosion-corrosion conditions , 2018, Corrosion Science.

[14]  Jionglong Su,et al.  Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles , 2015, Comput. Math. Methods Medicine.

[15]  Francesco Marcelloni,et al.  Detection of traffic congestion and incidents from GPS trace analysis , 2017, Expert Syst. Appl..

[16]  Liu Yang,et al.  Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network , 2019, Neural Computing and Applications.