# Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform

arXiv: https://arxiv.org/abs/1606.02031 · HTML: https://mlsanity.com/paper/1606.02031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2016.

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

- [Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection](https://mlsanity.com/paper/2101.02047/) (2021)
- [Benchmarking 2D Egocentric Hand Pose Datasets](https://mlsanity.com/paper/2409.07337/) (2024)
- [The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments](https://mlsanity.com/paper/2407.14649/) (2024)

## Abstract (arXiv)

Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion. We address this problem via a Hough transform based approach coupled with a discriminatively learned error-correcting component to tackle the well known issue of incorrect votes from the Hough transform. In this framework, local parts vote collectively for the start & end positions of each action over time. We also construct an in-house annotated dataset of 300 long videos, containing 3,177 single-action subsequences over 16 action classes collected from 26 individuals. Our system is empirically evaluated on this real-life dataset for both the action recognition and detection tasks, and is shown to produce satisfactory results. To facilitate reproduction, the new dataset and our implementation are also provided online.