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http://dx.doi.org/10.12815/kits.2018.17.1.123

A Multiple Vehicle Object Detection Algorithm Using Feature Point Matching  

Lee, Kyung-Min (Dept. of Computer Science., Univ. of Semyung)
Lin, Chi-Ho (Dept. of Computer Science., Univ. of Semyung)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.1, 2018 , pp. 123-128 More about this Journal
Abstract
In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.
Keywords
Feature point; FAST; Image segmentation; Mean-Shift;
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