# Review of Action Recognition and Detection Methods

arXiv: https://arxiv.org/abs/1610.06906 · HTML: https://mlsanity.com/paper/1610.06906/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2016.

## Built on by (most cited first, 13 of 13)

- [ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2103.15088/) (2021)
- [Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation](https://mlsanity.com/paper/2503.19622/) (2025)
- [Is Appearance Free Action Recognition Possible?](https://mlsanity.com/paper/2207.06261/) (2022)
- [Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context](https://mlsanity.com/paper/2103.16155/) (2021)
- [A Multi-viewpoint Outdoor Dataset for Human Action Recognition](https://mlsanity.com/paper/2110.04119/) (2021)
- [How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios](https://mlsanity.com/paper/2210.10039/) (2022)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning](https://mlsanity.com/paper/2102.00565/) (2021)
- [Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction](https://mlsanity.com/paper/2107.05140/) (2021)
- [Towards Activated Muscle Group Estimation in the Wild](https://mlsanity.com/paper/2303.00952/) (2023)
- [Detecting key Soccer match events to create highlights using Computer Vision](https://mlsanity.com/paper/2204.02573/) (2022)
- [Detecting Biological Locomotion in Video: A Computational Approach](https://mlsanity.com/paper/2105.12661/) (2021)
- [mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing](https://mlsanity.com/paper/2106.09164/) (2021)

## Abstract (arXiv)

In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.