CREST: Convolutional Residual Learning for Visual Tracking
本文概覽 分析DCF系列跟蹤算法的不足，並加以改進； 本文核心一：將DCF作爲CNN中的一個卷積層； 本文核心二：將特徵提取，響應圖產生以及模型更新整合到CNN中進行端對端訓練； 本文核心三：將殘差學習的思想用於深度目標跟蹤網絡的更新，可以更有效地應對目標外觀的大變化和小變化； 本文實驗效果：在OTB100上獲得了0.837的精度和0.623的覆蓋率； DCF算法分析 DCF算法優點一：可以在頻域
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- 8. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
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相關文章
- 1. CREST: Convolutional Residual Learning for Visual Tracking
- 2. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》論文筆記
- 3. 目標跟蹤-CREST: Convolutional Residual Learning for Visual Tracking
- 4. CREST: Convolutional Residual Learning for Visual Tracking ---- 文獻翻譯理解
- 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. 論文閱讀：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 10. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 閱讀筆記