...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Weakly Supervised Metric Learning for Traffic Sign Recognition in a LIDAR-Equipped Vehicle
【24h】

Weakly Supervised Metric Learning for Traffic Sign Recognition in a LIDAR-Equipped Vehicle

机译：配备激光雷达的车辆中的弱监督度量学习，用于交通标志识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We address the problem of traffic sign recognition in a light detection and ranging (LIDAR)-equipped vehicle. With the help of 3-D LIDAR points, the 2-D multiview sign images will be easily detected from the captured images of street signs. After detection, the sign recognition problem is formulated as a multiview object recognition task. We develop a metric-learning-based template matching approach for this task and learn a distance metric between the captured images and the corresponding sign templates. For each sign, recognition is done via soft voting by the recognition results of its corresponding multiview images. We propose a latent structural support vector machine (SVM)-based weakly supervised metric learning (WSMLR) method to learn the metric and a reliability classifier. The reliability classifier is used to determine each image's reliability, which serves as each image's weight in both the learning and soft voting procedure. We evaluate the proposed method for multiview traffic sign recognition on a multiview traffic sign data set with 112 categories and observe very encouraging results compared with other state-of-the-art methods. In addition, the method can be customized to solve the single-view sign recognition. The performance of our method for single-view sign recognition is tested on two public data sets, showing that our method is comparable with other competitive ones.
机译：我们解决了配备光检测和测距（LIDAR）的车辆中交通标志识别的问题。借助3-D LIDAR点，可以从路标的捕获图像中轻松检测到2-D多视图标志图像。检测后，将符号识别问题公式化为多视图对象识别任务。我们针对此任务开发了一种基于度量学习的模板匹配方法，并了解了所捕获图像与相应符号模板之间的距离度量。对于每个标志，通过对其相应的多视图图像的识别结果进行软投票来进行识别。我们提出了一种基于潜在结构支持向量机（SVM）的弱监督度量学习（WSMLR）方法来学习度量和可靠性分类器。可靠性分类器用于确定每个图像的可靠性，它在学习和软投票过程中均充当每个图像的权重。我们评估了针对具有112个类别的多视图交通标志数据集的多视图交通标志识别方法，并且与其他最新方法相比，观察到了令人鼓舞的结果。此外，该方法可以定制以解决单视图符号识别。在两个公共数据集上测试了我们用于单视图符号识别的方法的性能，这表明我们的方法可与其他竞争方法相媲美。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号