Fast Tracking of Moving Target Combining SURF and Cluster Analysis

Abstract:

In order to design a moving target fast tracking system with respect to a real-time and stable tracking process, especially when the shape of moving target or its environment condition changes, a new algorithm named SURF-KMs is proposed. SURF-KMs combines the advantages of SURF algorithm with a cluster analysis of K-means method. First, feature points are collected and then they generate the matching template vectors based on the SURF algorithm. Second, the feature points and the center of the target are estimated by using the K-means method to determine the target’s cluster scope and update the tracking window. Finally, a self-adapting updating strategy for matching template is also proposed in order to track moving target automatically. Experimental results indicate that SURF-KMs is mostly able to achieve a stable tracking while with the monitored target rotating, scale changing, and also the environment illumination glittering. Moreover, it can satisfy the system requirements of tracking stability, higher precision and anti-jamming.

• Periodical: Key Engineering Materials (Volume 552)
• Pages: 542-547
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.552.542
• Online since: May 2013
• Authors: Ying Li, Jing Jiang Yu, Yue Gang Fu, Zheng Ping Xu, Xun Zhou
• Keywords: Fast Tracking, Moving Target, SURF

References

[1] LINDEBERG T. Feature Detection with Automatic Scale Selection [J]. International Journal of Computer Vision, 1998, 30(2): 79-116.

[2] LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision(IJCV), 2004, 60(2): 91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

[3] JI Hua,WU Yuan-hao, SUN Hong-hai, WANG yan-jie .SIFT feature matching algorithm with global information[J].Optic and Precision Engineering，2009，17(2)：439-444. [4]

[4] BAY H, TUYTELAARS T, VAN Gool L. SURF: Speeded up robust features [C]. European Conference on Computer Vision(ECCV), 2006, 3951(3): 404-417.

DOI: 10.1007/11744023_32

[5] BAY H, TUYTELAARS T, VAn Gool L. Speeded-Up Robust Features(SURF) [J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.

DOI: 10.1016/j.cviu.2007.09.014

[6] Neto H V, Nehmzow U. Automated exploration and inspection: Comparing two visual novelty detectors [J]. International Journal of Advanced Robotic Systems，2005，2(4)：355-362.

DOI: 10.5772/5770

[7] LUO J, Oubong G. A comparision of SIFT, PCA-SIFT and SURF [J]. International Journal of Image Processing, 2009,3(4): 143-152.

[8] WANG Yong-ming, WANG Gui-jin. Image Local Invariant Features and Descripors[M].Bei Jing: National Defense Industry Press.2010.