# SMART-Vision: Survey of Modern Action Recognition Techniques in Vision

arXiv: https://arxiv.org/abs/2501.13066 · HTML: https://mlsanity.com/paper/2501.13066/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 88 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks](https://mlsanity.com/paper/2008.07404/) (2020)
- [Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition](https://mlsanity.com/paper/2010.09978/) (2020)
- [DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing](https://mlsanity.com/paper/1611.01942/) (2016)
- [Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition](https://mlsanity.com/paper/2007.14690/) (2020)
- [Revisiting Classifier: Transferring Vision-Language Models for Video Recognition](https://mlsanity.com/paper/2207.01297/) (2022)
- [Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching](https://mlsanity.com/paper/1911.04131/) (2019)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [P-ODN: Prototype based Open Deep Network for Open Set Recognition](https://mlsanity.com/paper/1905.01851/) (2019)
- [PMAL: Open Set Recognition via Robust Prototype Mining](https://mlsanity.com/paper/2203.08569/) (2022)
- [Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition](https://mlsanity.com/paper/2107.00606/) (2021)
- [Zero-Shot Action Recognition in Videos: A Survey](https://mlsanity.com/paper/1909.06423/) (2019)
- [M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning](https://mlsanity.com/paper/2409.15657/) (2024)
- [Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos](https://mlsanity.com/paper/1810.08691/) (2018)
- [Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events](https://mlsanity.com/paper/2005.04490/) (2020)
- [Towards Accurate Human Pose Estimation in Videos of Crowded Scenes](https://mlsanity.com/paper/2010.10008/) (2020)
- [From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition](https://mlsanity.com/paper/2402.12790/) (2024)
- [Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory](https://mlsanity.com/paper/2411.04501/) (2024)
- [ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition](https://mlsanity.com/paper/2308.03908/) (2023)

## Built on by (most cited first, 8 of 8)

- [Explainable AI: XAI-Guided Context-Aware Data Augmentation](https://mlsanity.com/paper/2506.03484/) (2025)
- [Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions](https://mlsanity.com/paper/2509.25539/) (2025)
- [SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.08016/) (2025)
- [Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming](https://mlsanity.com/paper/2509.16835/) (2025)
- [2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving](https://mlsanity.com/paper/2508.21080/) (2025)
- [Bilingual Word Level Language Identification for Omotic Languages](https://mlsanity.com/paper/2509.07998/) (2025)
- [Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models](https://mlsanity.com/paper/2602.16608/) (2026)
- [Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks](https://mlsanity.com/paper/2509.11493/) (2025)

## Abstract (arXiv)

Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dynamics of individuals' movements in videos. These patterns arise in sequential data, such as video frames, which are often essential to accurately distinguish actions that would be ambiguous in a single image. HAR has garnered considerable interest due to its broad applicability, ranging from robotics and surveillance systems to sports motion analysis, healthcare, and the burgeoning field of autonomous vehicles. While several taxonomies have been proposed to categorize HAR approaches in surveys, they often overlook hybrid methodologies and fail to demonstrate how different models incorporate various architectures and modalities. In this comprehensive survey, we present the novel SMART-Vision taxonomy, which illustrates how innovations in deep learning for HAR complement one another, leading to hybrid approaches beyond traditional categories. Our survey provides a clear roadmap from foundational HAR works to current state-of-the-art systems, highlighting emerging research directions and addressing unresolved challenges in discussion sections for architectures within the HAR domain. We provide details of the research datasets that various approaches used to measure and compare goodness HAR approaches. We also explore the rapidly emerging field of Open-HAR systems, which challenges HAR systems by presenting samples from unknown, novel classes during test time.