The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
객체 추적 커뮤니티에는 다양한 관찰 모델이 도입되었으며, 이들을 결합하는 것이 유망한 방향이 되었습니다. 본 논문에서는 다양한 관측 모델의 신뢰도를 추정한 다음 입자 필터 프레임워크에서 이를 효과적으로 결합하기 위한 새로운 접근 방식을 제안합니다. 우리의 접근 방식에서 공간적 우도 분포는 전반적인 유사성, 분포 선명도 및 다중 피크 정도를 반영하는 간단하지만 효율적인 세 가지 매개변수로 표현됩니다. 이 세 가지 측면의 균형은 신뢰도의 좋은 추정으로 이어지며, 이는 각 관찰 모델의 장점을 유지하는 데 도움이 되고 부분 교합에 대한 견고성을 더욱 향상시킵니다. 까다로운 비디오 시퀀스에 대한 실험은 우리 접근 방식의 효율성을 보여줍니다.
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Fan JIANG, Guijin WANG, Chang LIU, Xinggang LIN, Weiguo WU, "Robust Object Tracking via Combining Observation Models" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 3, pp. 662-665, March 2010, doi: 10.1587/transinf.E93.D.662.
Abstract: Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.662/_p
부
@ARTICLE{e93-d_3_662,
author={Fan JIANG, Guijin WANG, Chang LIU, Xinggang LIN, Weiguo WU, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Object Tracking via Combining Observation Models},
year={2010},
volume={E93-D},
number={3},
pages={662-665},
abstract={Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.},
keywords={},
doi={10.1587/transinf.E93.D.662},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Robust Object Tracking via Combining Observation Models
T2 - IEICE TRANSACTIONS on Information
SP - 662
EP - 665
AU - Fan JIANG
AU - Guijin WANG
AU - Chang LIU
AU - Xinggang LIN
AU - Weiguo WU
PY - 2010
DO - 10.1587/transinf.E93.D.662
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2010
AB - Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.
ER -