Intra color-shape classification for traffic sign recognition
This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and c...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2010
|
| Online Access: | http://hdl.handle.net/20.500.11937/43459 |
| _version_ | 1848756697696305152 |
|---|---|
| author | Lim, Hann Seng, K. Ang, L. |
| author_facet | Lim, Hann Seng, K. Ang, L. |
| author_sort | Lim, Hann |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate. ©2010 IEEE. |
| first_indexed | 2025-11-14T09:16:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-43459 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:16:19Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-434592017-09-13T14:01:05Z Intra color-shape classification for traffic sign recognition Lim, Hann Seng, K. Ang, L. This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate. ©2010 IEEE. 2010 Conference Paper http://hdl.handle.net/20.500.11937/43459 10.1109/COMPSYM.2010.5685432 restricted |
| spellingShingle | Lim, Hann Seng, K. Ang, L. Intra color-shape classification for traffic sign recognition |
| title | Intra color-shape classification for traffic sign recognition |
| title_full | Intra color-shape classification for traffic sign recognition |
| title_fullStr | Intra color-shape classification for traffic sign recognition |
| title_full_unstemmed | Intra color-shape classification for traffic sign recognition |
| title_short | Intra color-shape classification for traffic sign recognition |
| title_sort | intra color-shape classification for traffic sign recognition |
| url | http://hdl.handle.net/20.500.11937/43459 |