# Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation

arXiv: https://arxiv.org/abs/1904.08505 · HTML: https://mlsanity.com/paper/1904.08505/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

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

- [Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations](https://mlsanity.com/paper/2307.01533/) (2023)
- [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021)
- [Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2205.15862/) (2022)
- [Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN](https://mlsanity.com/paper/2406.15003/) (2024)
- [On the Evaluation Protocol of Gesture Recognition for UAV-based Rescue Operation based on Deep Learning: A Subject-Independence Perspective](https://mlsanity.com/paper/2602.17854/) (2026)
- [Dynamic Gesture Recognition](https://mlsanity.com/paper/2109.09396/) (2021)
- [DiG-Net: Enhancing Human-Robot Interaction through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics](https://mlsanity.com/paper/2505.24786/) (2025)
- [Can Large Language Models Detect Methodological Flaws? Evidence from Gesture Recognition for UAV-Based Rescue Operation Based on Deep Learning](https://mlsanity.com/paper/2604.14161/) (2026)

## Abstract (arXiv)

Due to the advance of technologies, machines are increasingly present in people's daily lives. Thus, there has been more and more effort to develop interfaces, such as dynamic gestures, that provide an intuitive way of interaction. Currently, the most common trend is to use multimodal data, as depth and skeleton information, to enable dynamic gesture recognition. However, using only color information would be more interesting, since RGB cameras are usually available in almost every public place, and could be used for gesture recognition without the need of installing other equipment. The main problem with such approach is the difficulty of representing spatio-temporal information using just color. With this in mind, we propose a technique capable of condensing a dynamic gesture, shown in a video, in just one RGB image. We call this technique star RGB. This image is then passed to a classifier formed by two Resnet CNNs, a soft-attention ensemble, and a fully connected layer, which indicates the class of the gesture present in the input video. Experiments were carried out using both Montalbano and GRIT datasets. For Montalbano dataset, the proposed approach achieved an accuracy of 94.58%. Such result reaches the state-of-the-art when considering this dataset and only color information. Regarding the GRIT dataset, our proposal achieves more than 98% of accuracy, recall, precision, and F1-score, outperforming the reference approach by more than 6%.