.png){kind=icon}
返回
Dual-stream collaborative tracking algorithm combined with reliable memory based update
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.01.046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most visual tracking algorithms based on Siamese network adopt a fixed template in the whole tracking process, which sacrifices the available target appearance information. To address this issue, we propose a dual-stream collaborative tracking algorithm combined with reliable memory based update in this paper. Specifically, we first conduct a multi-task convolutional network consisting of convolutional kernel fusion and semantic integration, hence enhancing the discriminability of the convolutional model. Secondly, a reliable template storage policy is developed to establish a dynamic memory queue that carries available appearance information. In addition, to improve the template adaptability against appearance variation, we train a dynamic update network by serving the memory queue as samples, thereby realizes the adaptive update of the object template. Finally, a dual-stream collaborative tracking framework is proposed to integrate the matching results of the initial branch and the updated branch in the response layer, which alleviates the limited representation problems with the fixed template. We perform a thorough performance evaluation on the OTB2013, OTB2015, DTB70, Temple-Color-128, VOT2016, VOT2018, UAV20L and LaSOT benchmarks. Extensive experimental results show that our proposed method outperforms several state-of-the-art algorithms and achieves real-time operations. CO 2022 Elsevier B.V. All rights reserved.
Keyword:
Siamese network based tracker
Reliable template storage
Template update
Dual-stream tracking
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking基于相关滤波器的动态显著性感知正则化目标跟踪
PREAI
分享收藏
分享收藏Fast Learning of Spatially Regularized and Content Aware Correlation Filter for Visual Tracking
PREAI
分享收藏
查看更多内容