译：Action Recognition with Improved Trajectories
改进轨迹的行动识别 摘要：最近，密集轨迹被证实是用于动做识别的有效视频表示，而且在各类数据集上实现了最早进的结果。本论文经过考虑相机的运动来纠正它们，从而改善了它们的性能。为了估计相机运动，咱们使用SURF描述符和密集光流来匹配帧之间的特征点，这些特征点被证实是互补的。而后，这些匹配用于使用RANSAC稳健地估计单应性。人体运动一般与相机运动不一样，并产生不一致的匹配。为了改进估计，使用人类检测器
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- 1. 译：Action Recognition with Improved Trajectories
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相关文章
- 1. 译：Action Recognition with Improved Trajectories
- 2. Action Recognition:iDT论文解读(Improved Dense Trajectories)
- 3. 译：Two-stream convolutional networks for action recognition in videos
- 4. 行为识别笔记：improved dense trajectories算法（iDT算法）
- 5. 行人属性“Contextual Action Recognition with R*CNN”
- 6. 论文翻译：Skeleton-Based Action Recognition with Directed Graph Neural Networks
- 7. 论文翻译：Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
- 8. 论文翻译：Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- 9. Spatiotemporal Residual Networks for Video Action Recognition
- 10. 【视频行为识别0】传统方法：Dense Trajectories(DT)和Improved Dense Trajectories(IDT)算法（ICCV2013）