Published in

Trans Tech Publications, Applied Mechanics and Materials, (556-562), p. 2792-2796, 2014

DOI: 10.4028/www.scientific.net/amm.556-562.2792

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Improved SURF Detection Combined with Dual FLANN Matching and Clustering Analysis

Journal article published in 2014 by Jun Fei Li, Geng Wang, Qiang Li
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

In this paper, an improved object detection method based on SURF (Speed-Up Robust Feature) is presented. SURF is a widely used method in computer vision. But it’s still not efficient enough to apply in real-time applications, such as real time object tracking. To reduce the time cost, the traditional descriptor of SURF is altered. Triangle and diagonal descriptor is adopted to replace the Haar wavelet calculation. Then dual matching approach based on FLANN is employed. Thus matching errors can be cut down. Besides, the traditional SURF does not give the accurate region of the target. To restrict the area, clustering analysis is used which is promoted from K-WMeans. Experimental work demonstrates the proposed approach achieve better effect than traditional SURF in real scenarios.