# Generic Tubelet Proposals for Action Localization

arXiv: https://arxiv.org/abs/1705.10861 · HTML: https://mlsanity.com/paper/1705.10861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

## Built on by (most cited first, 3 of 3)

- [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://mlsanity.com/paper/2308.01618/) (2023)
- [Point3D: tracking actions as moving points with 3D CNNs](https://mlsanity.com/paper/2203.10584/) (2022)
- [RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems](https://mlsanity.com/paper/2205.00364/) (2022)

## Abstract (arXiv)

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.