River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking
A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shado...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Hindawi
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/5625 |
| _version_ | 1848744848834691072 |
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| author | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn |
| author_facet | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn |
| author_sort | Lim, King Hann |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity. |
| first_indexed | 2025-11-14T06:07:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-5625 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:07:59Z |
| publishDate | 2012 |
| publisher | Hindawi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-56252017-09-13T16:02:57Z River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking Lim, King Hann Seng, Kah Phooi Ang, Li-Minn A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity. 2012 Journal Article http://hdl.handle.net/20.500.11937/5625 10.1155/2012/465819 Hindawi unknown |
| spellingShingle | Lim, King Hann Seng, Kah Phooi Ang, Li-Minn River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title | River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title_full | River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title_fullStr | River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title_full_unstemmed | River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title_short | River Flow Lane Detection and Kalman filtering-based B-spline Lane Tracking |
| title_sort | river flow lane detection and kalman filtering-based b-spline lane tracking |
| url | http://hdl.handle.net/20.500.11937/5625 |