# Spatio-temporal Action Recognition: A Survey

arXiv: https://arxiv.org/abs/1901.09403 · HTML: https://mlsanity.com/paper/1901.09403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://mlsanity.com/paper/2308.01618/) (2023)
- [Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations](https://mlsanity.com/paper/2110.11626/) (2021)
- [Multi-model learning by sequential reading of untrimmed videos for action recognition](https://mlsanity.com/paper/2401.14675/) (2024)
- [Query matching for spatio-temporal action detection with query-based object detector](https://mlsanity.com/paper/2409.18408/) (2024)
- [Human Stone Toolmaking Action Grammar (HSTAG): A Challenging Benchmark for Fine-grained Motor Behavior Recognition](https://mlsanity.com/paper/2410.08410/) (2024)

## Abstract (arXiv)

The task of action recognition or action detection involves analyzing videos and determining what action or motion is being performed. The primary subject of these videos are predominantly humans performing some action. However, this requirement can be relaxed to generalize over other subjects such as animals or robots. The applications can range from anywhere between human-computer inter-action to automated video editing proposals. When we consider spatiotemporal action recognition, we deal with action localization. This task not only involves determining what action is being performed but also when and where itis being performed in said video. This paper aims to survey the plethora of approaches and algorithms attempted to solve this task, give a comprehensive comparison between them, explore various datasets available for the problem, and determine the most promising approaches.