CREST: Convolutional Residual Learning for Visual Tracking

时间 2020-12-30

标签目标跟踪算法深度学习繁體版

原文原文链接

本文概览分析DCF系列跟踪算法的不足，并加以改进；本文核心一：将DCF作为CNN中的一个卷积层；本文核心二：将特征提取，响应图产生以及模型更新整合到CNN中进行端对端训练；本文核心三：将残差学习的思想用于深度目标跟踪网络的更新，可以更有效地应对目标外观的大变化和小变化；本文实验效果：在OTB100上获得了0.837的精度和0.623的覆盖率； DCF算法分析 DCF算法优点一：可以在频域

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1. CREST: Convolutional Residual Learning for Visual Tracking
2. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》论文笔记
3. 目标跟踪-CREST: Convolutional Residual Learning for Visual Tracking
4. CREST: Convolutional Residual Learning for Visual Tracking ---- 文献翻译理解
5. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
6. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
7. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
8. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
9. 论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
10. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 阅读笔记
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1. CREST: Convolutional Residual Learning for Visual Tracking
2. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》论文笔记
3. 目标跟踪-CREST: Convolutional Residual Learning for Visual Tracking
4. CREST: Convolutional Residual Learning for Visual Tracking ---- 文献翻译理解
5. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
6. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
7. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
8. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
9. 论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
10. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 阅读笔记

>>更多相关文章<<