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Learning Adaptively Context-Weight-Aware Correlation Filters for UAV Tracking with Robust Spatial-Temporal Regularization
Dongze, Hao1,2; Yinghao, Cai2; Yiping, Yang2; Jixiang, Zhang2
2021-06
Conference Name2021 The 4th International Conference on Image and Graphics Processing
Conference Date2021-01
Conference PlaceSanya, China(线上)
Abstract

Recently, Discriminative Correlation Filter (DCF) based methods have been widely applied in tracking for unmanned aerial vehicles (UAVs) because of their promising performance and efficiency. However, boundary effect, filter corruption, lack of context information and the poor representation of the object lead to the decrease in discriminability. In this paper, a novel learning adaptively contextweight-aware correlation filters with robust spatial-temporal regularization method (ACRST) is proposed. Both convolutional features and hand-crafted features are employed to improve representations for object appearances. Then the ACRST tracker extracts context samples around the object to help the filter be aware of the background information and adaptively learns the weights of these context patches. Thus, the tracker can improve the robustness against background noises especially for similar samples. Meanwhile, the tracker merges a robust spatial-temporal regularization to prevent the filter corruption and boundary effect. We design a center-attention spatial regularizer to focus on the valid information of the object better and we propose a method to obtain the value of the parameter of the temporal regularization adaptively. Extensive experiments have been conducted on 123 challenging UAV tracking sequences. The results prove that our tracker performs better than other state-of-the-art trackers

DOIhttps://doi.org/10.1145/3447587.3447599
Indexed ByEI
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44985
Collection综合信息系统研究中心_视知觉融合及其应用
Corresponding AuthorJixiang, Zhang
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Dongze, Hao,Yinghao, Cai,Yiping, Yang,et al. Learning Adaptively Context-Weight-Aware Correlation Filters for UAV Tracking with Robust Spatial-Temporal Regularization[C],2021.
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