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SVM based column-level approach for crosswalk detection in low-resolution images

机译:基于SVM的列级方法用于低分辨率图像中的人行横道检测

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Development of automated assistance systems for the visually impaired is still relatively uncommon research topic. However, the use of the cameras and image processing is widely spread and can be a good source of useful information for the visually impaired user. In this work, we are considering usage of SVM (Support Vector Machine) for detecting the crosswalk region in low-resolution images. The proposed approach uses SVM to make decisions on column level thereby providing the horizontal position of the crosswalk in image. Such information can potentially help the visually impaired person to cross the road safely. Preliminary testing was conducted and satisfying results were obtained especially in terms of performance.
机译:针对视觉障碍者的自动辅助系统的开发仍然是相对不常见的研究课题。但是,相机的使用和图像处理已广泛传播,并且可以成为视力障碍用户有用信息的良好来源。在这项工作中,我们正在考虑使用SVM(支持向量机)来检测低分辨率图像中的人行横道区域。所提出的方法使用SVM在列级别上做出决策,从而提供人行横道在图像中的水平位置。这些信息可能会帮助视障人士安全过马路。进行了初步测试,并获得了令人满意的结果,特别是在性能方面。

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