【論文翻譯】Multiple Object Tracking by Flowing and Fusing（CVPR2020）
Multiple Object Tracking by Flowing and Fusing 通過光流和融合實現多目標跟蹤 摘要： 大多數的多目標跟蹤(MOT)方法都是爲兩個子任務計算單獨的目標特徵:目標方向的運動估計和成對的再識別(Re-ID)。由於視頻幀之間的目標數目不確定，這兩個子任務在端到端深度神經網絡(DNNs)中都很難有效地擴展。針對上述問題，本文設計了一種端到端DNN跟蹤方法——流動
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相關文章
- 1. 【論文分享CVPR2020】Multiple Object Tracking by Flowing and Fusing
- 2. 論文筆記Multiple Object Tracking by Flowing and Fusing
- 3. [論文翻譯]ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking (CVPR2020)
- 4. Multiple Anchor Learning for Visual Object Detection論文翻譯 -- CVPR2020
- 5. 論文中英對照翻譯--（Fusing Multiple Deep Features for Face Anti-spoofing）
- 6. 論文翻譯：Multi-camera Multi-object Tracking
- 7. 論文筆記：Multiple Object Tracking: A Literature Review
- 8. 論文閱讀：《POI: Multiple Object Tracking with High Performance Detection and Appearance Feature 》
- 9. Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification 論文筆記
- 10. 論文閱讀Multiple People Tracking by Lifted Multicut and Person Re-identification