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
대상 템플릿과의 최대 유사성 일치 문제로 간주되는 Siamese 시각적 추적은 컴퓨터 비전에서 점점 더 많은 관심을 받고 있습니다. 그러나 실시간 추적의 정확성과 장시간 추적의 견고성 사이의 균형 요구를 충족하기 어렵다는 것이 현재 Siamese 추적기의 과제입니다. 이 작업은 강력한 상관 관계를 위한 하나의 초기 템플릿과 정확한 상관 관계를 위한 적응형 기능 최적 선택 기능을 갖춘 다른 임시 템플릿으로 구성된 이중 파이프라인 상관 융합 네트워크(ADF-SiamRPN으로 명명)를 갖춘 새로운 Siamese 기반 추적기를 제안합니다. . 이후 학습 가능한 상관-반응 융합 네트워크의 추진을 통해 추적 성능의 종합적인 개선을 추구하고 있습니다. ADF-SiamRPN의 성능을 최첨단 추적기와 비교하기 위해 OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT 및 TrackingNet과 같은 벤치마크에서 많은 실험을 수행합니다. 추적 실험 결과는 ADF-SiamRPN이 비교된 모든 추적기보다 성능이 뛰어나고 정확성과 견고성 사이에서 최상의 균형을 달성한다는 것을 보여줍니다.
Ying KANG
Defense Innovation Institute,Chinese People's Liberation Army
Cong LIU
National University of Defense Technology
Ning WANG
National University of Defense Technology
Dianxi SHI
Defense Innovation Institute,Tianjin Artificial Intelligence Innovation Center
Ning ZHOU
Chinese People's Liberation Army
Mengmeng LI
Defense Innovation Institute,Tianjin Artificial Intelligence Innovation Center
Yunlong WU
Defense Innovation Institute,Tianjin Artificial Intelligence Innovation Center
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Ying KANG, Cong LIU, Ning WANG, Dianxi SHI, Ning ZHOU, Mengmeng LI, Yunlong WU, "Siamese Visual Tracking with Dual-Pipeline Correlated Fusion Network" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1702-1711, October 2021, doi: 10.1587/transinf.2021EDP7060.
Abstract: Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7060/_p
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@ARTICLE{e104-d_10_1702,
author={Ying KANG, Cong LIU, Ning WANG, Dianxi SHI, Ning ZHOU, Mengmeng LI, Yunlong WU, },
journal={IEICE TRANSACTIONS on Information},
title={Siamese Visual Tracking with Dual-Pipeline Correlated Fusion Network},
year={2021},
volume={E104-D},
number={10},
pages={1702-1711},
abstract={Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.},
keywords={},
doi={10.1587/transinf.2021EDP7060},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Siamese Visual Tracking with Dual-Pipeline Correlated Fusion Network
T2 - IEICE TRANSACTIONS on Information
SP - 1702
EP - 1711
AU - Ying KANG
AU - Cong LIU
AU - Ning WANG
AU - Dianxi SHI
AU - Ning ZHOU
AU - Mengmeng LI
AU - Yunlong WU
PY - 2021
DO - 10.1587/transinf.2021EDP7060
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.
ER -