一种智能手机上的场景实时识别算法

桂振文; 陈靖; 刘越; 王涌天

doi:10.3724/SP.J.1004.2014.00083

一种智能手机上的场景实时识别算法

doi: 10.3724/SP.J.1004.2014.00083

1.
北京理工大学计算机科学与技术学院北京 100081;
2.
北京市混合现实与新型显示工程技术研究中心, 北京理工大学光电学院北京 100081

基金项目:

国家高技术研究发展计划（863计划）（2013AA013802）;国家自然科学基金（61072096）;国家科技重大专项基金（2012ZX03002004）资助

详细信息

作者简介:
桂振文北京理工大学计算机学院博士研究生. 主要研究方向为计算机视觉，图像处理和移动增强现实.E-mail：quizhenwen1983@bit.edu.cn

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出版历程
- 收稿日期: 2012-07-09
- 修回日期: 2013-03-19
- 刊出日期: 2014-01-20

A Real-time Recognition Algorithm of Scenes on Smartphones

1.
Department of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081;
2.
Beijing Engineering Research Center for Mixed Reality and Advanced Display, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081

Funds:

Supported by National High Technology Research and Development Program of China (863 Program) (2013AA013802), National Natural Science Foundation of China (61072096), and National Science and Technology Major Project of China (2012ZX0 3002004)

摘要

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.
- 移动检索 /
- SURF算法 /
- SIFT算法 /
- 智能手机
Abstract: 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.
- Mobile retrieval /
- SURF algorithm /
- SIFT algorithm /
- smartphone

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参考文献(19)

[1]	Chen D M, Baatz G, Koser K, Tai S S, Vedantham R, Pylvanaine T, Roimela K, Chen X, Bach J, Pollefeys M, Girod B, Grzeszczuk R. City-scale landmark identification on mobile devices. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2011. 737-744
[2]	Wang X Y, Yang M, Cour T, Zhu S H, Yu K, Han T X. Contextual weighting for vocabulary tree based image retrieval. In: Proceedings of the 2011 International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 209-216
[3]	Muralidharan R, Chandrasekar C. 3D object recognition using multiclass support vector machine——-K-nearest neighbor supported by local and global feature. Journal of Computer Science, 2012, 8(8): 1380-1388
[4]	Heo J P, Lee Y, He J F, Chang S F, Yoon S E. Spherical hashing. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 2957-2964
[5]	Mair E, Hager G D, Burschka D, Suppa M, Hirzinger G. Adaptive and generic corner detection based on the accelerated segment test. In: Proceedings of the 2010 European Conference on Computer Vision (ECCV). Berlin, Germany: Springer, 2010. 183-196
[6]	Leutenegger S, Chli M, Siegwart R Y. Brisk: binary robust invariant scalable keypoints. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2548-2555
[7]	Rosten E, Drummond T. Machine learning for high-speed corner detection. In: Proceedings of the 9th European conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2006. 430-443
[8]	Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[9]	Bay H, Ess A, Tuytelaars T, Van Gool L. SURF: Speeded-up robust features (SURF). Computer Vision and Image Understanding, 2008, 110(3): 346-359
[10]	Calonder M, Lepetit V, Ozuysal M, Trzcinski T, Strecha C, Fua P. BRIEF: Computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1281-1298
[11]	Yuan Z W, Wang Y H. Research on K nearest neighbor non-parametric regression algorithm based on KD-tree and clustering analysis. In: Proceedings of the 4th International Conference on Computational and Information Sciences. Chongqing, China: IEEE, 2012. 298-301
[12]	Koonsanit K, Jaruskulchai C, Eiumnoh A. Parameter-free K-means clustering algorithm for satellite imagery application. In: Proceedings of the 2012 International Conference on Information Science and Applications. Suwon, Korea: IEEE, 2012. 1-6
[13]	Yamashita T, Yamauchi Y, Fujiyoshi H. Human detection for multiple pose by boosted randomized trees. In: Proceedings of the 1st Asian Conference on Pattern Recognition. Beijing, China: IEEE, 2011. 229-233
[14]	Ozuysal M, Calonder M, Lepetit V, Fua P. Fast keypoint recognition using random ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(3): 448-461
[15]	Wagner D, Reitmayr G, Mulloni A, Drummond T W, Schmalstieg D. Real-time detection and tracking for augmented reality on mobile phones. IEEE Transactions on Visualization and Computer Graphics, 2010, 16(3): 355-368
[16]	Chen W C, Xiong Y G, Gao J, Gelfand N, Grzeszczuk R. Efficient extraction of robust image features on mobile devices. In: Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. Nara, Japan: IEEE, 2007. 287-288
[17]	Gu J, Mukundan R, Billinghurst M. Developing mobile phone AR applications using J2ME. In: Proceedings of the 23rd International Conference on Image and Vision Computing New Zealand. Christchurch, New Zealand: IEEE, 2008. 1-6
[18]	Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: Proceedings of the 10th International Conference on Computer Vision. Beijing, China: IEEE, 2005. 1508-1515
[19]	Nister D, Stewenius H. Scalable recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 2161-2168