基于图像识别的室内定位系统研究

发布时间：2018-06-14 13:26

本文选题：SURF + 向量空间模型　；参考：《电子科技大学》2017年硕士论文

【摘要】：最近几年来图像信息识别和处理的技术越来越受到关注,在学术方面有研究越来越多的成果,在日常生活场景、工业、医学、航空航天等方面都得到广泛的应用,图像信息识别技术尚且还有很多的潜在应用场景和研究意义。于此同时,21世纪以来,包括Apple、Samsung和各大国产手机厂商的智能手机产品的图像采集性能和计算能力飞速增长和移动智能手机设备的普及,人们越来越依赖手机这样的设备来提供各种服务。本文分析了在公共建筑,例如商场环境下,位置表示非常有局限性的情况,并且室内收不到GPS信号的情况下,通过使用智能手机实现室内定位功能成为一个热门应用场景。在基于图像信息识别的室内定位方法中,图像信息识别算法中,图像的匹配技术是最关键的一步,图像匹配技术的速率和匹配成功率是影响图像信别效果的重要因素。本文借鉴了伯克利大学的“基于图像处理的城市环境定位研究”中使用的定位系统模型,提出了更适合室内环境的定位系统模型和相对高效的解决办法。本文根据此场景,提出了一系列解决方案,主要的研究方向有以下几个方面:本文详细分析SIFT和SURF算法的理论方法和实现方式,针对室内环境的场景特点,对比和分析了直接使用SIFT和SURF匹配的优点和缺点。基于SURF特征匹配提出了向量空间模型,并且引入了支持向量机算法,实现了图像的分类,通过在地图上标注出匹配图像的位置,实现了室内定位系统。详细对比和分析了基于SURF特征值匹配的系统和基于向量空间模型分类器系统的优缺点。通过实验证明了采用SURF特征匹配算法和向量空间模型的分类系统在室内定位应用中在匹配速率和配准率上都有良好表现。综上所述,本文研究了基于图像信息识别的室内定位系统,针对室内的应用场景特点,提出基于SURF的向量空间模型分类系统,详细分析了基于SURF特征的匹配系统的优点,并针对此算法进行了相应的仿真实验,证明了其准确性和时效性。
[Abstract]:In recent years, more and more attention has been paid to the technology of image information recognition and processing. More and more achievements have been made in academic research. They have been widely used in daily life scenes, industry, medicine, aerospace and so on. Image information recognition technology still has many potential applications and research significance. At the same time, since the 21st century, the image acquisition performance and computing power of smartphone products, including Apple Samsung and major domestic mobile phone manufacturers, have grown rapidly and mobile smartphone devices have become popular. People are increasingly relying on devices like mobile phones to provide a variety of services. In this paper, it is analyzed that the location representation is very limited in public buildings, such as shopping malls, and the GPS signal can not be received indoors, so it becomes a hot application scene to realize indoor positioning by using smart phone. In the indoor localization method based on image information recognition, the image matching technology is the most important step in the image information recognition algorithm. The rate and success rate of image matching technology are the important factors that affect the effect of image information recognition. This paper draws lessons from the location system model used in the study of Urban Environment location based on Image processing of Berkeley University, and puts forward a more suitable location system model for indoor environment and a relatively efficient solution. According to this scenario, this paper puts forward a series of solutions, the main research directions are as follows: this paper analyzes the theoretical methods and implementation methods of sift and surf algorithm in detail, aiming at the characteristics of indoor environment. The advantages and disadvantages of direct use of sift and surf are compared and analyzed. A vector space model based on surf feature matching is proposed, and support vector machine (SVM) algorithm is introduced to realize the classification of images. The indoor positioning system is implemented by marking the location of the matching image on the map. The advantages and disadvantages of the system based on surf eigenvalue matching and vector space model classifier are compared and analyzed in detail. It is proved by experiments that the classification system based on surf feature matching algorithm and vector space model has good performance in both matching rate and registration rate in indoor localization applications. To sum up, this paper studies the indoor positioning system based on image information recognition. According to the characteristics of indoor application scene, a vector space model classification system based on SURF is proposed, and the advantages of the matching system based on SURF feature are analyzed in detail. The simulation results show that the algorithm is accurate and time-efficient.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TN929.53

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