Traffic sign and license plate detection based on saliency, meanshift, and mathematical morphology
| dc.contributor.author | Cheong; Wei Sheik | |
| dc.date.accessioned | 2021-03-16T08:11:04Z | |
| dc.date.available | 2021-03-16T08:11:04Z | |
| dc.date.issued | 2015-09-01 | |
| dc.description.abstract | An object detection model that consists of cepstrum saliency, mean-shift segmentation, and morphological shape estimation is proposed in this research. An improved computational saliency method based on human visual attention is introduced. Cepstrum saliency is based on the principles of de-convolution in the log-spectrum domain, and is computationally fast with only single parameter to tune. Moreover, cepstrum saliency exhibits color consistency under various illuminations, where the normalized RGB color scheme can be used for color images. To further enhance the proposed object detection model, non-parametric mean-shift and Otsu’s method are utilized for figure-ground segmentation. Besides that, simple shape factors based on mathematical morphology are introduced to identify the segmented objects by measuring shapes. To evaluate the effectiveness and applicability of the proposed method, two problems in the transportation section, i.e., traffic sign and license plate detection, were studied in detail. Based on two publicly available and locally collected data sets, the proposed detection method demonstrates a good equipoise between accuracy and speed. The simulation results indicate that it is seven times faster than shape descriptors in traffic sign detection, and has an average of less than 0.6 s in license plate detection as compared with template matching and machine learning methods. The findings indicate the usefulness of the proposed object detection method in providing a unified framework for both traffic sign and license plate detection problems; therefore contributing towards advancement in intelligent transportation systems. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12189 | |
| dc.language.iso | en | en_US |
| dc.title | Traffic sign and license plate detection based on saliency, meanshift, and mathematical morphology | en_US |
| dc.type | Thesis | en_US |
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