Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields论文简单解读
（提要：这是卡内基梅隆大学即Detect-and-Track论文后又一篇姿态跟踪的论文，本文的算法运用到了卡内基梅隆大学在姿态估计中OpenPose的PAFs部分亲和场的问题。） 摘要： 1.提出了一个可跨视频序列编码的预测时空亲和场的结构（Spatio-Temporal Affinity Fields (STAF) 2.提出了一种跨肢体连接的结构图 3.从以前帧的STAFheatmap来估计当前
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相关文章
- 1. Pose flow: Efficient Online Pose Tracking 论文解读
- 2. 《Pose Flow: Efficient Online Pose Tracking》论文解读之PoseFlow
- 3. Pose Flow: Efficient Online Pose Tracking
- 4. 论文阅读--Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 5. Openpose论文阅读 | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 6. 【论文阅读】Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 7. Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields解读
- 8. # Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 9. Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 10. 《OpenPose：Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields》论文笔记