.png){kind=icon}
返回
Learning dual-margin model for visual tracking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2021.04.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing trackers usually exploit robust features or online updating mechanisms to deal with target variations which is a key challenge in visual tracking. However, the features being robust to variations remain little spatial information, and existing online updating methods are prone to overfitting. In this paper, we propose a dual-margin model for robust and accurate visual tracking. The dual-margin model comprises an intra-object margin between different target appearances and an inter-object margin between the target and the background. The proposed method is able to not only distinguish the target from the background but also perceive the target changes, which tracks target appearance changing and facilitates accurate target state estimation. In addition, to exploit rich off-line video data and learn general rules of target appearance variations, we train the dual-margin model on a large off-line video dataset. We perform tracking under a Siamese framework using the constructed appearance set as templates. The proposed method achieves accurate and robust tracking performance on five public datasets while running in real-time. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of the proposed algorithm. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Visual tracking
Siamese network
Dual margin
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:
6.3
论文数:
8.2K
被引数:
3.0W
机构
引用论文
.png){kind=icon}
Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏Effect of particle size on degree of inversion in ferrites investigated by Mössbauer spectroscopy
PREAI
分享收藏
查看更多内容