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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bi Song Han Liqun Han Cunwu Sun Dehui Li Zhijun Lei Zhenwu Zhai Weifeng |
| Copyright Year | 2013 |
| Description | Author affiliation: Beijing Key Lab. of Fieldbus Technol. & Autom., North China Univ. of Technol., Beijing, China (Bi Song; Han Cunwu; Sun Dehui; Li Zhijun; Lei Zhenwu; Zhai Weifeng) || Center for Intell. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China (Han Liqun) |
| Abstract | Traffic state is a key parameter for traffic guidance system, which has been considered as a promise way to relieve the traffic congestion. This paper presented a method to acquire traffic state information covering large road net by analyzing traffic surveillance video. A new background model was proposed which is based on fuzzy ART and forgetting mechanism to solve the problem in this domain. A learning quantization vector (LVQ) was used as the classifier to map the traffic parameters into a certain class. According to the test results, the accuracy produced by the suggested method is 91.5%, and this means that the method is feasible and practical. |
| Starting Page | 3634 |
| Ending Page | 3638 |
| File Size | 480255 |
| Page Count | 5 |
| File Format | |
| ISBN | 9789881563835 |
| ISSN | 19341768 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | TCCT, CAA |
| Subject Keyword | Computational modeling Roads Subspace constraints Neurons Traffic state recognition Vectors Jamming Forget mechanism Fuzzy ART based background model Vehicles |
| Content Type | Text |
| Resource Type | Article |
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