Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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High Efficient Viola-Jones Detection Framework for Real-Time Object Detection

실시간 물체 검출을 위한 고효율 Viola-Jones 검출 프레임워크

  • Received : 2013.12.10
  • Accepted : 2014.01.15
  • Published : 2014.03.31
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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.

본 연구에서는 기존의 Viola-Jones 검출 프레임워크를 개선하여 하나의 특징 당 더 높은 효율을 가지며 검출대상이 아닌 서브 윈도우들을 더 빠르게 제거하는 개선된 학습 알고리즘을 제안한다. 학습의 결과로 생성된 물체 검출기는 서브윈도우를 특정 임계값까지 빠르게 제거하기 때문에 서브윈도우당 계산수가 줄어든다. 기존의 Viola-Jones 물체 검출기와 동일한 프레임워크이므로 검출 성능에는 영향을 주지 않는다. MIT-CMU 테스트 집합에 대해서 서브윈도우당 특징 계산 횟수를 측정하였으며 기존 계산 횟수의 45.5%로 줄어들어 검출 속도가 약 58.5% 향상됨을 확인하였다.

Keywords

References

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