# Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System

arXiv: https://arxiv.org/abs/2107.12744 · HTML: https://mlsanity.com/paper/2107.12744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition](https://mlsanity.com/paper/1904.04289/) (2019)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Compressed Video Action Recognition](https://mlsanity.com/paper/1712.00636/) (2017)
- [Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos](https://mlsanity.com/paper/1703.10664/) (2017)
- [What Actions are Needed for Understanding Human Actions in Videos?](https://mlsanity.com/paper/1708.02696/) (2017)
- [Lattice Long Short-Term Memory for Human Action Recognition](https://mlsanity.com/paper/1708.03958/) (2017)
- [AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos](https://mlsanity.com/paper/1611.08240/) (2016)
- [ReHAR: Robust and Efficient Human Activity Recognition](https://mlsanity.com/paper/1802.09745/) (2018)
- [A Hybrid Framework for Action Recognition in Low-Quality Video Sequences](https://mlsanity.com/paper/1903.04090/) (2019)

## Abstract (arXiv)

With the rapid increase in digital technologies, most fields of study include recognition of human activity and intention recognition, which are essential in smart environments. In this study, we equipped the activity recognition system with the ability to recognize intentions by affecting the pace of movement of individuals in the representation of images. Using this technology in various environments such as elevators and automatic doors will lead to identifying those who intend to pass the automatic door from those who are passing by. This system, if applied in elevators and automatic doors, will save energy and increase efficiency. For this study, data preparation is applied to combine the spatial and temporal features with the help of digital image processing principles. Nevertheless, unlike previous studies, only one AlexNet neural network is used instead of two-stream convolutional neural networks. Our embedded system was implemented with an accuracy of 98.78% on our intention recognition dataset. We also examined our data representation approach on other datasets, including HMDB-51, KTH, and Weizmann, and obtained accuracy of 78.48%, 97.95%, and 100%, respectively. The image recognition and neural network models were simulated and implemented using Xilinx simulators for the Xilinx ZCU102 board. The operating frequency of this embedded system is 333 MHz, and it works in real-time with 120 frames per second (fps).