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Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking

文献类型：期刊论文

作者Yu, Bin2,3; Tang, Ming2; Zhu, Guibo2,3; Wang, Jinqiao1,2,3; Lu, Hanqing2,3
刊名SENSORS
出版日期2021-12-01
卷号21期号:23页码:14
关键词visual tracking bounding box estimation overlap maximization distribution calibration
DOI10.3390/s21238100
通讯作者Yu, Bin(bin.yu@nlpr.ia.ac.cn)
英文摘要Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.
资助项目Key-Areas Research and Development Program of Guangdong Province[2020B010165001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[62076235] ; National Natural Science Foundation of China[62002356] ; Open Research Projects of Zhejiang Lab[2021KH0AB07]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
出版者MDPI
WOS记录号WOS:000734610900001
资助机构Key-Areas Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Open Research Projects of Zhejiang Lab
源URL[http://ir.ia.ac.cn/handle/173211/47151]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者Yu, Bin
作者单位1.ObjectEye Inc, Beijing 100078, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Bin,Tang, Ming,Zhu, Guibo,et al. Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking[J]. SENSORS,2021,21(23):14.
APA Yu, Bin,Tang, Ming,Zhu, Guibo,Wang, Jinqiao,&Lu, Hanqing.(2021).Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking.SENSORS,21(23),14.
MLA Yu, Bin,et al."Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking".SENSORS 21.23(2021):14.

入库方式： OAI收割

来源：自动化研究所

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