An Intelligent Recognition Algorithm on Traffic Safety States

Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural network recognition model based on prototype pattern by analyzing various methods on intelligent video processing. Our proposed method reali...

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Veröffentlicht in:Applied Mechanics and Materials Jg. 433-435; H. Advances in Mechatronics and Control Engineering II; S. 1388 - 1391
Hauptverfasser: Wang, Wei Zhi, Liu, Bing Han
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Zurich Trans Tech Publications Ltd 15.10.2013
Schlagworte:
Accuracy
Algorithms
Classification
Dangerous
Image processing
Neural networks
Object recognition
Pattern recognition
Recognition
Traffic accidents & safety
Traffic models
Traffic safety
Video
Video data
Traffic Safety State
Automatic Recognition
Intelligent Analysis
ISBN:303785894X, 9783037858943
ISSN:1660-9336, 1662-7482, 1662-7482
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Abstract Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural network recognition model based on prototype pattern by analyzing various methods on intelligent video processing. Our proposed method realizes real time classification of traffic safety states with high accuracy of traffic safety states recognition. The experimental results validate that the accuracy of classification of proposed method arrives at 87.5%, increased by 16.2% compared to traditional neural network methods.
AbstractList Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural network recognition model based on prototype pattern by analyzing various methods on intelligent video processing. Our proposed method realizes real time classification of traffic safety states with high accuracy of traffic safety states recognition. The experimental results validate that the accuracy of classification of proposed method arrives at 87.5%, increased by 16.2% compared to traditional neural network methods.
Author Liu, Bing Han
Wang, Wei Zhi
Author_xml – givenname: Wei Zhi
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  organization: Fuzhou University : College of Computer
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Cites_doi 10.1016/j.aap.2010.03.021
10.1016/j.aap.2010.03.013
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Issue Advances in Mechatronics and Control Engineering II
Keywords Traffic Safety State
Automatic Recognition
Intelligent Analysis
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Notes Selected, peer reviewed papers from the 2013 2nd International Conference on Mechatronics and Control Engineering (ICMCE 2013), August 28-29, 2013, Guangzhou, China
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Snippet Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural...
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SubjectTerms Accuracy
Algorithms
Classification
Dangerous
Image processing
Neural networks
Object recognition
Pattern recognition
Recognition
Traffic accidents & safety
Traffic models
Traffic safety
Video
Video data
Title An Intelligent Recognition Algorithm on Traffic Safety States
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