• 제목/요약/키워드: target perceivability

검색결과 2건 처리시간 0.017초

표적의 부분가림이 존재하는 환경에서 견실한 추적을 위한 영상 표적 탐지, 추적 알고리듬 연구 (A Study of Image Target Detection and Tracking for Robust Tracking in an Occluded Environment)

  • 김용;송택렬
    • 제어로봇시스템학회논문지
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    • 제16권10호
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    • pp.982-990
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    • 2010
  • In a target tracking system using image information from a CCD (Charged Couple Device) or an IIR (Imaging Infra-red) sensor, occluded targets can result in track losses. If the target is occlued by background objects such as buildings or trees, probability of track existence will be reduced sharply and track will be terminated due to track maintenance algorithms. This paper proposes data association algorithm based on target existence for the robust tracking performance. we suggest the HPDA (Highest Probability Data Association) algorithm based on target existence and the tracking performance is compared with the established method based on target perceivability. Image tracking simulation that utilizes virtual 3D images and real IR images is employed to evaluate the robustness of the proposed tracking algorithm.

표적이 일시적으로 가려지는 환경에서 ITS 기법을 이용한 영상 표적 추적 알고리듬 연구 (A Study of Image Target Tracking Using ITS in an Occluding Environment)

  • 김용;송택렬
    • 제어로봇시스템학회논문지
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    • 제19권4호
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    • pp.306-314
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    • 2013
  • Automatic tracking in cluttered environment requires the initiation and maintenance of tracks, and track existence probability of true track is kept by Markov Chain Two model of target existence propagation. Unlike Markov Chain One model for target existence propagation, Markov Chain Two model is made up three hypotheses about target existence event which are that the target exist and is detectable, the target exists and is non-detectable through occlusion, and the target does not exist and is non-detectable according to non-existing target. In this paper we present multi-scan single target tracking algorithm based on the target existence, which call the Integrated Track Splitting algorithm with Markov Chain Two model in imaging sensor.