# Video Action Recognition Using spatio-temporal optical flow video frames

arXiv: https://arxiv.org/abs/2103.05101 · HTML: https://mlsanity.com/paper/2103.05101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [A Comprehensive Study of Deep Video Action Recognition](https://mlsanity.com/paper/2012.06567/) (2020)
- [Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection](https://mlsanity.com/paper/1704.00616/) (2017)
- [Tiny Video Networks](https://mlsanity.com/paper/1910.06961/) (2019)
- [Hidden Two-Stream Convolutional Networks for Action Recognition](https://mlsanity.com/paper/1704.00389/) (2017)
- [Accuracy and Performance Comparison of Video Action Recognition Approaches](https://mlsanity.com/paper/2008.09037/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities](https://mlsanity.com/paper/2409.09678/) (2024)
- [Two-Stream temporal transformer for video action classification](https://mlsanity.com/paper/2601.14086/) (2026)

## Abstract (arXiv)

Recognizing human actions based on videos has became one of the most popular areas of research in computer vision in recent years. This area has many applications such as surveillance, robotics, health care, video search and human-computer interaction. There are many problems associated with recognizing human actions in videos such as cluttered backgrounds, obstructions, viewpoints variation, execution speed and camera movement. A large number of methods have been proposed to solve the problems. This paper focus on spatial and temporal pattern recognition for the classification of videos using Deep Neural Networks. This model takes RGB images and Optical Flow as input data and outputs an action class number. The final recognition accuracy was about 94%.