Saliency and Active Contour based Traffic Sign Detection
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@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:
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