Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Multiple Vehicles Tracking via sequential posterior estimation

순차적인 사후 추정에 의한 다중 차량 추적

  • Lee, Won-Ju (The Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Yoon, Chang-Young (The Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Lee, Hee-Jin (The Department of Information Control Engineering, Hankyong National University) ;
  • Kim, Eun-Tai (The Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Park, Mignon (The Department of Electrical and Electronic Engineering, Yonsei University)
  • 이원주 (연세대학교 전기전자공학과) ;
  • 윤창용 (연세대학교 전기전자공학과) ;
  • 이희진 (국립한경대학교 정보제어공학과) ;
  • 김은태 (연세대학교 전기전자공학과) ;
  • 박민용 (연세대학교 전기전자공학과)
  • Published : 2007.01.25
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Abstract

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be 'distracted' causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

운전자를 위한 영상처리 시스템에서 도로 위의 움직이는 물체와 고정된 물체의 구분은 매우 중요한 문제이다. 많은 연구자들이 색상과 경계 기반의 추적 시스템은 'distracted' 현상으로 인해 잘못된 결과를 야기 시키는 데 이것은 동시에 모든 점들이 예상을 벗어난 경우에 대한 문제를 다루지 않기 때문이다. 본 논문에서는 순차적인 몬테카를로 필터를 사용하여 다중 차량 추적에 대응하며 광학적 흐름 기법의 명암 흐름과 히스토그램 기법의 색상 정보의 분포를 결합하여 실시간 시스템의 강인성과 정확성을 향상시킨다. 또한 고정된 물체의 경우 적응하는 입자 수의 밀도를 줄여 시간이 지남에 따라 추적 대상에서 제외된다. 두 개의 큰 흐름으로 나뉘는데 전자는 움직이는 물체와 고정된 물체를 구분하기 위한 예측 단계에 대하여 설명하고 후자는 센서인 영상으로부터 얻어진 정보를 측정 단계로 사용하여 겹쳐진 영역에 대응하는 방법에 대하여 논한다.

Keywords

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