STAIR Actions: A Video Dataset of Everyday Home Actions

TL;DR

STAIR Actions is a large-scale, fine-grained video dataset of everyday home actions designed to advance human action recognition research in domestic contexts.

Contribution

The paper introduces STAIR Actions, a new extensive dataset with 102,462 videos across 100 home action categories for improved action recognition.

Findings

Effective training of large models on STAIR Actions

Achieved good performance in action recognition tasks

Compared favorably to existing datasets

Abstract

A new large-scale video dataset for human action recognition, called STAIR Actions is introduced. STAIR Actions contains 100 categories of action labels representing fine-grained everyday home actions so that it can be applied to research in various home tasks such as nursing, caring, and security. In STAIR Actions, each video has a single action label. Moreover, for each action category, there are around 1,000 videos that were obtained from YouTube or produced by crowdsource workers. The duration of each video is mostly five to six seconds. The total number of videos is 102,462. We explain how we constructed STAIR Actions and show the characteristics of STAIR Actions compared to existing datasets for human action recognition. Experiments with three major models for action recognition show that STAIR Actions can train large models and achieve good performance. STAIR Actions can be downloaded from http://actions.stair.center