# ReHAR: Robust and Efficient Human Activity Recognition

arXiv: https://arxiv.org/abs/1802.09745 · HTML: https://mlsanity.com/paper/1802.09745/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System](https://mlsanity.com/paper/2107.12744/) (2021)
- [Detecting Violence in Video Based on Deep Features Fusion Technique](https://mlsanity.com/paper/2204.07443/) (2022)
- [Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies](https://mlsanity.com/paper/2607.07957/) (2026)

## Abstract (arXiv)

Designing a scheme that can achieve a good performance in predicting single person activities and group activities is a challenging task. In this paper, we propose a novel robust and efficient human activity recognition scheme called ReHAR, which can be used to handle single person activities and group activities prediction. First, we generate an optical flow image for each video frame. Then, both video frames and their corresponding optical flow images are fed into a Single Frame Representation Model to generate representations. Finally, an LSTM is used to pre- dict the final activities based on the generated representations. The whole model is trained end-to-end to allow meaningful representations to be generated for the final activity recognition. We evaluate ReHAR using two well-known datasets: the NCAA Basketball Dataset and the UCFSports Action Dataset. The experimental results show that the pro- posed ReHAR achieves a higher activity recognition accuracy with an order of magnitude shorter computation time compared to the state-of-the-art methods.