中文摘要：

目前常用的SIFT和SURF识别算法存在匹配时间长、运算量大和内存占用多等问题，无法满足实时移动检索应用．针对这些问题，本文提出了一种智能手机上的实时识别算法，通过缩短特征点检测时间和降低尺度空间特征点定位的复杂度，保证识别的实时性和准确性．实验结果表明，本算法能有效地运行在普通的资源受限智能手机上，具有较好的通用性；同时能实现对场景的实时识别，消耗内存资源也较少，适合在实际应用中使用．

英文摘要：

Currently, the used SIFT and SURF algorithms cannot meet the demand of higher real-time identification applications, and these algorithms have a lot of problems, including a long matching time, a large amount of memory usage and computational complexity and so on. In this paper, we propose a method for real-time recognition on a smartphone, through shortening the time of feature point detection and reducing the complexity of feature point location on scale space to ensure real-time identification and accuracy. The experimental results show that this algorithm can effectively run on resource-constrained ordinary smartphone with good versatility. At the same time, it can achieve real-time recognition of the scene and consume less memory resources, so it is suitable for using in practical applications.