Research on method of feature extraction and recognition of road condition from nighttime video without vehicle segmentation

Acquirement of night traffic information of road net is very important to network comprehensive utilization improvement. Due to interference of vehicle lights at night, it is very difficult to segment vehicles from nighttime traffic video. This paper suggested the average brightness and traffic spee...

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Bibliographic Details
Published in2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems Vol. 1; pp. 6 - 10
Main Authors Song Bi, Dehui Sun, Liqun Han, Zhe Dong, Zhenwu Lei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Brightness
Cameras
Feature extraction
Jamming
Manuals
Nighttime traffic video processing
Road condition feature extraction
Road condition recognition
Roads
Traffic engineering
Vehicles
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Summary:Acquirement of night traffic information of road net is very important to network comprehensive utilization improvement. Due to interference of vehicle lights at night, it is very difficult to segment vehicles from nighttime traffic video. This paper suggested the average brightness and traffic speed in observing region to characterize traffic states, and analysis the effectiveness of parameters. Meanwhile, the extraction of these parameters do not need to vehicle segmentation. Based on RBF neural network, we achieve automatic road condition recognition, and obtain a higher coincidence rate with manual classification.
ISSN:2376-5933
2376-595X
DOI:10.1109/CCIS.2012.6664356