# Curvature: A signature for Action Recognition in Video Sequences

arXiv: https://arxiv.org/abs/1904.13003 · HTML: https://mlsanity.com/paper/1904.13003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

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

- [Towards Efficient Graph Convolutional Networks for Point Cloud Handling](https://mlsanity.com/paper/2104.05706/) (2021)
- [Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type](https://mlsanity.com/paper/2109.03783/) (2021)
- [UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks](https://mlsanity.com/paper/2508.19647/) (2025)

## Abstract (arXiv)

In this paper, a novel signature of human action recognition, namely the curvature of a video sequence, is introduced. In this way, the distribution of sequential data is modeled, which enables few-shot learning. Instead of depending on recognizing features within images, our algorithm views actions as sequences on the universal time scale across a whole sequence of images. The video sequence, viewed as a curve in pixel space, is aligned by reparameterization using the arclength of the curve in pixel space. Once such curvatures are obtained, statistical indexes are extracted and fed into a learning-based classifier. Overall, our method is simple but powerful. Preliminary experimental results show that our method is effective and achieves state-of-the-art performance in video-based human action recognition. Moreover, we see latent capacity in transferring this idea into other sequence-based recognition applications such as speech recognition, machine translation, and text generation.