| 基于多级Sigmoid神经网络的城市交通场景理解 |
| |
| 引用本文： | 谭论正,夏利民,夏胜平.基于多级Sigmoid神经网络的城市交通场景理解[J].国防科技大学学报,2012,34(4):132-137. |
| |
| 作者姓名： | 谭论正 夏利民 夏胜平 |
| |
| 作者单位： | 1. 中南大学信息科学与工程学院,湖南长沙,410075
2. 国防科技大学ATR重点实验室,湖南长沙,410073 |
| |
| 基金项目： | 国家863计划项目，国家自然科学基金项目，国家教育部博士点基金项目 |
| |
| 摘 要： | 交通场景的理解是交通监控、汽车安全辅助驾驶的基础.提出一种基于多级Sigmoid神经网络的城市交通环境理解方法.将5个3D结构特征与物体外观特征相结合表征城市交通环境,为了提高交通环境识别率,采用多级Sigmoid神经网络(MSNN)进行图像分割与识别.在公共测试视频数据库CamVid dataset 进行实验,实验结果表明了该方法的有效性.
|
| 关 键 词： | 空间结构特征 城市交通场景 多级Sigmoid神经网络 |
| 收稿时间： | 2011/9/19 0:00:00 |
Urban traffic scene understanding based on multi-level sigmoidal neural network |
| |
| TAN Lunzheng,XIA Limin and XIA Shengping.Urban traffic scene understanding based on multi-level sigmoidal neural network[J].Journal of National University of Defense Technology,2012,34(4):132-137. |
| |
| Authors: | TAN Lunzheng XIA Limin and XIA Shengping |
| |
| Institution: | 1College of Information Science and Engineering,Central South University,Chang sha 41007,China; 2.ATR Key Lab,National University of defense Technology,Changsha 410073,China) |
| |
| Abstract: | Urban traffic scene understanding is the basis of traffic monitoring and safety driving assistant system.A novel approach to understanding urban traffic scene captured from a car-mounted camera is proposed based on multi-level Sigmoidal neural network.Five 3D structure features were combined with the appearance features to represent the urban traffic environment and the recognition accuracy of traffic environment was improved by utilizing multi-level Sigmoidal neural network(MSNN) to segment and recognize the input images.Tested by the public CamVid dataset,the experimental results demonstrate the efficiency of the proposed approach. |
| |
| Keywords: | 3D structure urban traffic scene multi-level sigmoidal neural network |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《国防科技大学学报》浏览原始摘要信息 |
| 点击此处可从《国防科技大学学报》下载免费的PDF全文 |
