# Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset

arXiv: https://arxiv.org/abs/2210.07400 · HTML: https://mlsanity.com/paper/2210.07400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Learning Features by Watching Objects Move](https://mlsanity.com/paper/1612.06370/) (2016)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [Action Recognition using Visual Attention](https://mlsanity.com/paper/1511.04119/) (2015)
- [Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification](https://mlsanity.com/paper/1711.08200/) (2017)
- [Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks](https://mlsanity.com/paper/1612.00390/) (2016)
- [Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice](https://mlsanity.com/paper/1405.4506/) (2014)

## Abstract (arXiv)

In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in real-time even on low-powered systems such as a Raspberry Pi. The cornerstone of the proposed algorithm is the incorporation of both spatial and temporal information, as well as the information of the objects in a video while using an efficient architecture, and Optical Flow computation to achieve commendable results in real-time. The results achieved by this algorithm are benchmarked on the UCF-101 as well as the HMDB-51 datasets, achieving an accuracy of 92.7% and 64.9% respectively. An important point to note is that the algorithm is novel in the aspect that it is also able to learn the intricate differences between extremely similar actions, which would be difficult even for the human eye. Additionally, noticing a dearth in the number of datasets for the recognition of very similar or fine-grained actions, this paper also introduces a new dataset that is made publicly available, the Hand Wash Dataset with the intent of introducing a new benchmark for fine-grained action recognition tasks in the future.