基于高维特征表示的交通场景识别

刘文华; 李浥东; 王涛; 邬俊; 金一

doi:10.11959/j.issn.2096-6652.201943

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基于高维特征表示的交通场景识别

学术论文 | 更新时间：2024-06-05

- 基于高维特征表示的交通场景识别
- Transportation scene recognition based on high level feature representation
- 智能科学与技术学报 2019年1卷第4期页码：392-399
- 作者机构：
- 作者简介：
  
  [ "刘文华（1990- ），女，北京交通大学博士生，主要研究方向为计算机视觉、大数据分析、数据挖掘" ]
  [ "李浥东（1982- ），男，博士，北京交通大学计算机学院教授、副院长，主要研究方向为智能大数据分析、隐私保护、并行计算、计算机视觉" ]
  [ "王涛（1980- ），男，博士，北京交通大学计算机与信息技术学院副教授，主要研究方向为机器学习与计算机视觉" ]
  [ "邬俊（1981- ），男，博士，北京交通大学计算机与信息技术学院副教授，主要研究方向为机器学习理论及其在信息检索与推荐系统中的应用" ]
  [ "金一（1982- ），女，博士，北京交通大学计算机与信息技术学院副教授，博士生导师，主要研究方向为以异质人脸识别为代表的计算机视觉、模式识别理论和方法" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61672088)
- DOI：10.11959/j.issn.2096-6652.201943
  中图分类号： TP30
- 网络出版日期：2019-12，
  
  纸质出版日期：2019-12-20
- 稿件说明：
移动端阅览
刘文华, 李浥东, 王涛, 等. 基于高维特征表示的交通场景识别[J]. 智能科学与技术学报, 2019,1(4):392-399.

Wenhua LIU, Yidong LI, Tao WANG, et al. Transportation scene recognition based on high level feature representation[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 392-399.
刘文华, 李浥东, 王涛, 等. 基于高维特征表示的交通场景识别[J]. 智能科学与技术学报, 2019,1(4):392-399. DOI： 10.11959/j.issn.2096-6652.201943.

Wenhua LIU, Yidong LI, Tao WANG, et al. Transportation scene recognition based on high level feature representation[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 392-399. DOI： 10.11959/j.issn.2096-6652.201943.

摘要

随着智能交通的发展，快速、精确识别交通场景成为亟待解决的重要问题。目前已有许多识别方法可以提高交通场景的识别效果，但这些算法无法提取视觉概念的交通语义特征，导致识别精度低下。为此，设计了一种提取高维场景语义特征和结构信息的识别算法，以提高识别精度。为减少图像高维与低维特征表示之间的“语义鸿沟”，首先构建了一个场景类的语义描述系统，然后通过最小化损失（element-wise logistic loss）函数训练多标签分类网络，获取交通场景图像的高维特征表示，最后在4个大规模场景识别数据集上进行验证，实验结果显示，新算法在识别性能上优于其他的方法。

Abstract

With the development of intelligent transportation

it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years

a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition

however

most of these algorithms cannot extract the semantic characteristics of the concept of vision

leading to the low recognition accuracy in traffic scenes.Therefore

a novel traffic scene recognition algorithm which extracts the high-level semantic and structural information for improving the accuracy was proposed.A system to discover semantically meaningful descriptions of the scene classes to reduce the “semantic gap” between the high level and the low-level feature representation was built.Then

the multi-label network was trained by minimizing loss function (namely

element-wise logistic loss) to obtain the high-level semantic representation of traffic scene images.Finally

experiments on four large-scale scene recognition datasets show that the proposed algorithm considerably outperforms other state-of-the-art methods.

关键词

Keywords

references

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