| 基于支持向量机多分类器的室内外场景感知算法 |
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| 引用本文： | 阮锦佳,罗丹,罗海勇.基于支持向量机多分类器的室内外场景感知算法[J].计算机应用,2015,35(11):3135-3138. |
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| 作者姓名： | 阮锦佳 罗丹 罗海勇 |
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| 作者单位： | 1. 北京邮电大学 软件学院, 北京 100083;2. 北京邮电大学 国际学院, 北京 100083;3. 中国科学院计算技术研究所 普适计算研究中心, 北京 100190 |
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| 基金项目： | 国家自然科学基金资助项目(61374214);国家国际科技合作与交流专项课题(2015DFG12520);天津市863成果转化课题(14RCHZGX00857);深圳市战略性新兴产业发展专项(深发改[2014]1787号). |
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| 摘 要： | 针对普适室内外场景持续感知面临的低功耗、复杂动态环境、异构使用模式带来的挑战,提出了一种轻量级的基于支持向量机多分类器的高精度、低功耗室内外场景检测算法.该算法使用智能手机集成的各种传感器(可见光传感器、磁传感器、加速度传感器、陀螺仪传感器和气压传感器),在挖掘分析各种传感器在室内外场景的不同特征,以及人们在室内外场景的行为差异基础上,根据时间和气象条件设计多个支持向量机分类器,对复杂室内外场景进行识别.实验结果表明,基于支持向量机多分类器的室内外场景检测算法具有较好的普适性,可获得95%以上的室内外判定准确率,平均功耗小于5 mW.
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| 关 键 词： | 室内外场景识别 行为识别 支持向量机 多分类器 属性 |
| 收稿时间： | 2015-06-17 |
| 修稿时间： | 2015-08-19 |
Indoor and outdoor scene recognition algorithm based on support vector machine multi-classifier |
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| RUAN Jinjia,LUO Dan,LUO Haiyong.Indoor and outdoor scene recognition algorithm based on support vector machine multi-classifier[J].journal of Computer Applications,2015,35(11):3135-3138. |
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| Authors: | RUAN Jinjia LUO Dan LUO Haiyong |
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| Affiliation: | 1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100083, China;2. International School, Beijing University of Posts and Telecommunications, Beijing 100083, China;3. Research Centre of Pervasive Computing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | Considering the low power consumption for successive indoor and outdoor scenes pervasive perception in complex and dynamic environment, a lightweight indoor and outdoor scene identification algorithm based on Support Vector Machine (SVM) multi-classifier was proposed, which can accurately distinguish the indoor and outdoor scenes with low power consumption. The algorithm adopted data mining method to obtain different characteristics in indoor and outdoor scenes from the sensors integrated in smart phones (such as visible light sensors, magnetic sensors, acceleration sensors, gyro sensors, and pressure sensors, etc.). It also made advantage of human behavior difference between indoor and outdoor scene. According to different time and weather conditions, the algorithm designed support vector machine multi-classifier to identify complex indoor and outdoor scenes based on the differences of human behavior in indoor and outdoor scene. The simulation results show that the proposed algorithm has good universality, and can determine the indoor and outdoor scenes with more than 95% accuracy, and only consumes less than 5 mW averaging power. |
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| Keywords: | indoor and outdoor scene recognition activity recognition Support Vector Machine (SVM) multi-classifier feature |
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