New Hybrid Technique for traffic sign recognition
A hybrid traffic sign recognition scheme combining of knowledge-based analysis and radial basis function neural classifier (RBFNN) is proposed in this paper. Initially, traffic signs are detected from the road scenes using color segmentation method. The extracted signs are then passed to the recogni...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2008
|
| Online Access: | http://hdl.handle.net/20.500.11937/3097 |
| _version_ | 1848744137751265280 |
|---|---|
| author | Lim, Hann Ang, L. Seng, K. |
| author_facet | Lim, Hann Ang, L. Seng, K. |
| author_sort | Lim, Hann |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A hybrid traffic sign recognition scheme combining of knowledge-based analysis and radial basis function neural classifier (RBFNN) is proposed in this paper. Initially, traffic signs are detected from the road scenes using color segmentation method. The extracted signs are then passed to the recognition system for classification. The proposed recognition technique composes of three stages: (i) color histogram classification, (ii) shape classification and, (iii) RBF neural classification. Based on the unique color and shape of traffic signs, they can be classified into smaller subclasses and can be easily recognized using RBFNN. Before feeding traffic sign into the RBFNN, traffic sign features are extracted by Principle Component Analysis (PCA) in order to reduce the dimensionality of the original images. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. The performance of the proposed hybrid system is evaluated and compared to the purely neural classifier. The experimental results demonstrate that the proposed method has better recognition rate. |
| first_indexed | 2025-11-14T05:56:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-3097 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:56:41Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-30972017-09-13T14:33:01Z New Hybrid Technique for traffic sign recognition Lim, Hann Ang, L. Seng, K. A hybrid traffic sign recognition scheme combining of knowledge-based analysis and radial basis function neural classifier (RBFNN) is proposed in this paper. Initially, traffic signs are detected from the road scenes using color segmentation method. The extracted signs are then passed to the recognition system for classification. The proposed recognition technique composes of three stages: (i) color histogram classification, (ii) shape classification and, (iii) RBF neural classification. Based on the unique color and shape of traffic signs, they can be classified into smaller subclasses and can be easily recognized using RBFNN. Before feeding traffic sign into the RBFNN, traffic sign features are extracted by Principle Component Analysis (PCA) in order to reduce the dimensionality of the original images. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. The performance of the proposed hybrid system is evaluated and compared to the purely neural classifier. The experimental results demonstrate that the proposed method has better recognition rate. 2008 Conference Paper http://hdl.handle.net/20.500.11937/3097 10.1109/ISPACS.2009.4806678 restricted |
| spellingShingle | Lim, Hann Ang, L. Seng, K. New Hybrid Technique for traffic sign recognition |
| title | New Hybrid Technique for traffic sign recognition |
| title_full | New Hybrid Technique for traffic sign recognition |
| title_fullStr | New Hybrid Technique for traffic sign recognition |
| title_full_unstemmed | New Hybrid Technique for traffic sign recognition |
| title_short | New Hybrid Technique for traffic sign recognition |
| title_sort | new hybrid technique for traffic sign recognition |
| url | http://hdl.handle.net/20.500.11937/3097 |