CVPR 2018 FlowTrack:《End-to-end Flow Correlation Tracking with Spatial-temporal Attention》论文笔记
理解出错之处望不吝指正。 本文模型叫做FlowTrack。本文将光流信息和特征表示结合在一起进行目标跟踪，据作者所说，这是第一个在CNN based tracking中使用光流的模型。 光流的warp公式如下（p代表一个2维位置（x，y））： aggregation后得到的结果： 上式中的w是一个自适应权值，由文中提出的spatial-temporal
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相关文章
- 1. 论文阅读笔记 End-to-end Flow Correlation Tracking with Spatial-temporal Attention [2018 CVPR] (FlowTrack)
- 2. CVPR 2018 STRCF:《Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking》论文笔记
- 3. CVPR 2018 MCCT:《Multi-Cue Correlation Filters for Roubust Visual Tracking》论文笔记
- 4. 【目标跟踪】FlowTrack：End-to-end Flow Correlation Tracking with Spatial-temporal Attention
- 5. CVPR 2017 CA:《Context-Aware Correlation Filter Tracking》论文笔记
- 6. ECCV 2012 KCF/DCF:《High-speed tracking with kernelized correlation filters》论文笔记
- 7. CVPR 2018 LSART:《Learning Spatial-Aware Regressions for Visual Tracking》论文笔记
- 8. High-speed Tracking with Kernelized Correlation filters笔记
- 9. CVPR 2018 Siam-RPN:《High Performance Visual Tracking with Siamese Region Proposal Network》论文笔记
- 10. CVPR 2018 SINT++:《SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation》论文笔记