We develop a novel framework for action localization in videos. We propose the Tube Proposal Network (TPN), which can generate generic, class-independent, video-level tubelet proposals in videos. The generated tubelet proposals can be utilized in various video analysis tasks, including recognizing and localizing actions in videos. In particular, we integrate these generic tubelet proposals into a unified temporal deep network for action classification. Compared with other methods, our generic tubelet proposal method is accurate, general, and is fully differentiable under a smoothL1 loss function. We demonstrate the performance of our algorithm on the standard UCF-Sports, J-HMDB21, and UCF-101 datasets. Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets.

本文提出了一种新的基于视频的行动定位框架，使用管道提案网络(TPN)生成通用的、独立于类别、视频级别的管状建议，可用于各种视频分析任务，包括行动识别和定位。与其他方法相比，我们的通用管状建议方法准确、通用，并在平滑L1损失函数下完全可微。在标准的UCF-Sports、J-HMDB21和UCF-101数据集上证明了算法的性能。我们的独立于类别的TPN优于其他管状建议方法，我们的统一时间深度网络在所有三个数据集上都实现了最先进的定位结果。

动作定位的通用管道提议