Accurate Scale Estimation for Robust Visual Tracking(DSST) 一些理解

时间 2021-01-10

标签 dsst 栏目 CSS 繁體版

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个人理解 DSST 总体与kcf类似。在位置计算的基础上添加了缩放尺度回归。其中位置的回归参考kcf 缩放尺度回归原理如下： 1.在原尺度框的大小为基础 1，向上乘以scale_factor 得到几个尺度，向下除以scale_factor得到几个尺度 2.每个尺度的图像都缩放到原尺度 3.对每个尺度做hog特征提取，假设hog特征为16*16*32 其中32位hog特征的

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1. Multihypothesis Trajectory Analysis for Robust Visual Tracking
2. 《Online Filtering Training Samples for Robust Visual Tracking》解析
3. Robust Dense Mapping for Large-Scale Dynamic Environments
4. Robust Real-Time Extreme Head Pose Estimation
5. Counterfactual Samples Synthesizing for Robust Visual Question Answering相关知识和理解
6. 论文解读1-LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
7. 论文解读2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
8. Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation
9. Robust multi-scale ORB algorithm in real-time monocular visual odometry
10. FADNet:A Fast and Accurate Network for Disparity Estimation的论文翻译

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