| 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.[点击复制] |
| TAN Lunzheng,XIA Limin,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[点击复制] |
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| 基于多级Sigmoid神经网络的城市交通场景理解 |
| 谭论正1, 夏利民1, 夏胜平2 |
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(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)
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| 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 |
| DOI： |
| 投稿日期：2011-09-19 |
| 基金项目:国家863计划项目(2009AA11Z205);国家自然科学基金项目(50808025);国家教育部博士点基金项目(20090162110057) |
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| Urban traffic scene understanding based on multi-level sigmoidal neural network |
| TAN Lunzheng1, XIA Limin1, XIA Shengping2 |
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(1.College of Information Science and Engineering，Central South University ,Changsha 410075, China;2.ATR Key Lab, National University of defense Technology, Changsha 410073, China)
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| 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 |
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