.png){kind=icon}
返回
Spatio-temporal matching for siamese visual tracking
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2022.11.093.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Siamese trackers formulate the visual tracking task as a similarity matching problem through cross cor-relation. It is arduous for such methods to track targets with the presence of distractors. We suspect the reasons are twofold: 1) The irrelevant activated channels in the correlation map will produce ambiguous matching results. 2) The pipeline is a per-frame matching process and cannot handle the response aber-rance caused by temporal context variation. In this paper, we propose a spatio-temporal matching pro-cess to thoroughly explore the capability of 4-D matching in space (height, width and channel) and time. In spatial matching, we introduce a space-variant instance-aware correlation (SI-Corr) to implement dif-ferent channel-wise response recalibration for each matching position. SI-Corr can guide the generation of instance-aware features and distinguish the target and distractors at the instance level. In temporal matching, we design an aberrance repressed module (ARM) to investigate the short-term positional rela-tionship between the target and distractors. ARM utilizes a simple optimization method to restrict the abrupt alteration of the interframe response maps, which allows the network to learn a temporal consis-tency of context structure distribution. Moreover, we efficiently embed temporal consistency into the inference process. Experiments on six benchmarks, including OTB100, VOT2018, VOT2020, GOT-10k, LaSOT and TrackingNet, demonstrate the state-of-the-art performance of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Visual tracking
Siamese network
Spatio-temporal matching
Instance-aware
Temporal consistency
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
Sustaining the efficiency of the Fe(0)/H2O system for Cr(VI) removal by MnO2 amendment.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏
查看更多内容