Accurate Scale Estimation for Robust Visual Tracking(DSST) 一些理解
個人理解 DSST 總體與kcf類似。 在位置計算的基礎上添加了縮放尺度迴歸。其中位置的迴歸參考kcf 縮放尺度迴歸原理如下： 1.在原尺度框的大小爲基礎 1，向上乘以scale_factor 得到幾個尺度，向下除以scale_factor得到幾個尺度 2.每個尺度的圖像都縮放到原尺度 3.對每個尺度做hog特徵提取，假設hog特徵爲16*16*32 其中32位hog特徵的
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相關文章
- 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相關知識和理解
- 6. 論文解讀1-LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
- 7. 論文解讀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的論文翻譯