• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference >Deep Fusion Feature for Vehicle Classification and Recognition
【24h】

Deep Fusion Feature for Vehicle Classification and Recognition

机译：用于车辆分类和识别的深度融合功能

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To solve the problem of higher energy consumption and lower recognition rate caused by artificial extraction feature, which is sophisticated and single generally in the process of vehicle classification and identification, in this paper, we propose a method for video vehicle processing using deep fusion feature based on deep learning. This approach mixes all of the tail characteristics of vehicles after each Sampling layer together, including global characteristics of low-dimensional and local sparse response of high-dimensional, this allows us to get more information about the features, then softmax classifier is used for classification steps. Experimental results show that the final classification accuracy can be achieved about 99.32% by combining the characteristics of a single treatment with features of various strata after Sampling layers, which called full feature fusion. The algorithm we suggested is applicable to identify highway video vehicles, the recognition speed and accuracy of this method are improved significantly compared with the general means.
机译：针对人工提取特征导致的能量消耗高，识别率低的问题，本文提出了一种基于深度融合特征的视频车辆处理方法，该方法通常在车辆的分类和识别过程中较为复杂，单一。在深度学习上。这种方法将每个采样层之后的所有车辆尾部特征混合在一起，包括低维的全局特征和高维的局部稀疏响应，这使我们可以获得有关特征的更多信息，然后使用softmax分类器进行分类脚步。实验结果表明，结合单一处理的特征和采样层后各层的特征，最终分类精度可以达到约99.32％，这称为全特征融合。我们提出的算法适用于高速公路视频车辆的识别，与常规方法相比，该方法的识别速度和准确度均有明显提高。

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