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Urban Road Congestion Recognition Using Multi-Feature Fusion of Traffic Images

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DOI: 10.23977/jaip.2016.11002 | Downloads: 106 | Views: 6877


Hua Cui 1, Pannong Li 1, Zefa Wei 1, Xinxin Song 1, Lu Guo 1


1 School of Information Engineering, Chang’an University, Xi’an 710064, China

Corresponding Author

Lu Guo


Traffic congestions happen more and more frequently on the current urban roads. Detecting the congestion rapidly and effectively can avoid the second damages. In this paper, we use the traffic images as data source instead of the videos to detect traffic congestions, which have the advantages of low cost and big probability to be applied widely. Firstly, the interest region of the traffic images are calibrated manually, and then the image features in the interest region are abstracted, including the sift corner, gray histogram variance, gray level co-occurrence matrix of energy and contrast. Finally, BP neural network is used to realize image multi-feature fusion, and to classify the traffic condition described by the traffic images. The simulation results show that the method can recognize the traffic condition with the accuracy of 95%.


Traffic condition recognition, Image processing, Feature extraction, Sift corner, BP neural network


Lu, G. , Hua, C. , Pannong, L. , Zefa, W. and Xinxin, S. (2016) Urban Road Congestion Recognition Using Multi-Feature Fusion of Traffic Images. Journal of Artificial Intelligence Practice (2016) 1: 20-24.


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