대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

웨이블릿 기반의 신경망과 불변 모멘트를 이용한 실시간 이동물체 인식 및 추적 방법

Real-time Moving Object Recognition and Tracking Using The Wavelet-based Neural Network and Invariant Moments

  • 발행 : 2008.07.25
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 실시간 감시 시스템을 위한 웨이블릿(wavelet) 기반의 신경망과 불변 모멘트를 이용한 이동물체 인식과 추적 방법을 제안한다. 제안한 방법의 첫 번째인 움직임 후보영역 검출 단계에서는 연속된 두 프레임간의 차영상 분석 방법을 기반으로 하여 물체의 움직임에 의해 화소값 변화가 발생한 후보영역을 검출한다. 두 번째인 물체 인식 단계에서는 검출된 후보영역에 웨이블릿 신경망(wavelet neural network: WNN) 기반의 인식 방법을 사용하여 추적하고자하는 물체가 포함되어 있는지를 판별한다. 세 번째인 물체 추적 단계에서는 인식된 물체에 웨이블릿 불변 모멘트(invariant moments) 기반의 매칭 방법을 사용하여 인식된 이동 물체를 추적한다. 영상내에서 이동물체를 검출하기 위해 본 논문에서는 이전 영상과 현재 영상간의 화소밝기 차이에서 적응적 임계값(adaptive threholding)을 사용하여 주위 환경 변화에 강인한 이동물체 검출이 가능하였다. 또한 물체의 인식과 추적을 위해 웨이블릿 특징값을 사용함으로써, 계산 시간의 감소와 영상의 잡음에 의한 영향을 최소화시킬 수 있을 뿐만 아니라, 물체 인식 정확도가 향상되었다. 제안한 방법을 일반 도로에서 획득한 영상에서 실험한 결과, 자동차 검출율은 92.8%, 프레임당 처리 시간은 0.24초이다. 이것을 통해 제안한 방법은 실시간 지능형 교통 감시 시스템에 유용하게 적용될 수 있음을 알 수 있다.

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

키워드

참고문헌

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