# Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze

arXiv: https://arxiv.org/abs/1710.00187 · HTML: https://mlsanity.com/paper/1710.00187/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation](https://mlsanity.com/paper/2501.15071/) (2025)

## Abstract (arXiv)

Unsupervised segmentation of action segments in egocentric videos is a desirable feature in tasks such as activity recognition and content-based video retrieval. Reducing the search space into a finite set of action segments facilitates a faster and less noisy matching. However, there exist a substantial gap in machine understanding of natural temporal cuts during a continuous human activity. This work reports on a novel gaze-based approach for segmenting action segments in videos captured using an egocentric camera. Gaze is used to locate the region-of-interest inside a frame. By tracking two simple motion-based parameters inside successive regions-of-interest, we discover a finite set of temporal cuts. We present several results using combinations (of the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains egocentric videos depicting several daily-living activities. The quality of the temporal cuts is further improved by implementing two entropy measures.