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http://dx.doi.org/10.7471/ikeee.2014.18.1.001

High Efficient Viola-Jones Detection Framework for Real-Time Object Detection  

Park, Byeong-Ju (Dept. of Computer Engineering, Hanbat University)
Lee, Jae-Heung (Dept. of Computer Engineering, Hanbat University)
Publication Information
Journal of IKEEE / v.18, no.1, 2014 , pp. 1-7 More about this Journal
Abstract
In this paper, we suggest an improved Viola-Jones detection framework for the efficient feature selection and the fast rejection method of the sub-window. Our object detector has low computational complexity because it rejects sub-windows until specific threshold. Owing to using same framework, detection performance is same with the existing Viola-Jones detector. We measure the number of average feature calculation about MIT-CMU test set. As a result of the experiment, the number of average feature calculation is reduced to 45.5% and the detection speed is improved about 58.5% compared with the previous algorithm.
Keywords
High Efficient Feature; Fast Rejection; Object Detection; Machine Learning; Viola-Jones;
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Times Cited By KSCI : 2  (Citation Analysis)
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