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A Framework of Context-A ware Object Recognition for Smart Home

机译:智能家居的上下文感知对象识别框架

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摘要

Services for smart home share a fundamental problem-object recognition, which is challenging because of complex background and appearance variation of object. In this paper we develop a framework of object recognition for smart home integrating SIFT (scale invariant feature transform) and context knowledge of home environment. The context knowledge includes the structure and settings of a smart home, knowledge of cameras, illumination, and location. We counteract sudden significant illumination change by trained support vector machine (SVM) and use the knowledge of home settings to define the region for multiple view registration of an object. Experiments show that the trained SVM can recognize and distinguish different illumination classes which significantly facilitate object recognition.
机译:智能家居服务共享一个基本的问题对象识别,这是一个挑战,因为复杂的背景和对象的外观变化。在本文中,我们开发了一个集成了SIFT(尺度不变特征变换)和家庭环境上下文知识的智能家居对象识别框架。上下文知识包括智能家居的结构和设置,相机知识,照明和位置。我们通过训练有素的支持向量机(SVM)来抵消突然的明显照明变化,并使用家庭设置的知识来定义对象的多视图配准区域。实验表明,训练有素的SVM可以识别和区分不同的照明类别,从而极大地促进了物体识别。

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