full
search.png

Search

close.png

Cancel

arrow
Volume 7, Issue 3
Saliency and Active Contour based Traffic Sign Detection

Shangbing Gao and Yunyang Yan

J. Info. Comput. Sci. , 7 (2012), pp. 235-240.

Preview Purchase PDF 66 829

Cited by

google scholar semantic scholar
Export citation
  • Abstract
In this paper, we propose a new approach to detect salient traffic signs, which is based on visual saliency and auto-generated strokes for image segmentation. The proposed algorithm deals with two tasks on detecting traffic signs: auto-location and auto extraction. Firstly, inspired by recent work of visual saliency detection, we obtain the location of traffic signs in a natural image by multi-scale principle component analysis (MPCA). Secondly, in order to extract traffic signs, auto-generated strokes are used instead of drawing the strokes by the users, the sign board area is extracted using localizing Region-Based Active Contour. Extensive experiments on public datasets show that our approach outperforms state-of-the-art methods remarkably in salient traffic sign detection. Moreover, the proposed detection method has higher accurate rate and robustness to different natural scenes.
  • Keywords

Multiscale Image segmentation active contour traffic sign

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JICS-7-235, author = {Shangbing Gao and Yunyang Yan}, title = {Saliency and Active Contour based Traffic Sign Detection}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {7}, number = {3}, pages = {235--240}, abstract = {In this paper, we propose a new approach to detect salient traffic signs, which is based on visual saliency and auto-generated strokes for image segmentation. The proposed algorithm deals with two tasks on detecting traffic signs: auto-location and auto extraction. Firstly, inspired by recent work of visual saliency detection, we obtain the location of traffic signs in a natural image by multi-scale principle component analysis (MPCA). Secondly, in order to extract traffic signs, auto-generated strokes are used instead of drawing the strokes by the users, the sign board area is extracted using localizing Region-Based Active Contour. Extensive experiments on public datasets show that our approach outperforms state-of-the-art methods remarkably in salient traffic sign detection. Moreover, the proposed detection method has higher accurate rate and robustness to different natural scenes. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22647.html} }
TY - JOUR T1 - Saliency and Active Contour based Traffic Sign Detection AU - Shangbing Gao and Yunyang Yan JO - Journal of Information and Computing Science VL - 3 SP - 235 EP - 240 PY - 2024 DA - 2024/01 SN - 7 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22647.html KW - Multiscale KW - Image segmentation KW - active contour KW - traffic sign AB - In this paper, we propose a new approach to detect salient traffic signs, which is based on visual saliency and auto-generated strokes for image segmentation. The proposed algorithm deals with two tasks on detecting traffic signs: auto-location and auto extraction. Firstly, inspired by recent work of visual saliency detection, we obtain the location of traffic signs in a natural image by multi-scale principle component analysis (MPCA). Secondly, in order to extract traffic signs, auto-generated strokes are used instead of drawing the strokes by the users, the sign board area is extracted using localizing Region-Based Active Contour. Extensive experiments on public datasets show that our approach outperforms state-of-the-art methods remarkably in salient traffic sign detection. Moreover, the proposed detection method has higher accurate rate and robustness to different natural scenes.
Shangbing Gao and Yunyang Yan. (2024). Saliency and Active Contour based Traffic Sign Detection. Journal of Information and Computing Science. 7 (3). 235-240. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard