HaGRID - HAnd Gesture Recognition Image Dataset
Alexander Kapitanov, Karina Kvanchiani, Alexander Nagaev, Roman, Kraynov, Andrei Makhliarchuk

TL;DR
HaGRID is a large, diverse hand gesture image dataset designed to improve gesture recognition systems by including static and dynamic gestures, with extensive annotations and real-world variability.
Contribution
The paper introduces HaGRID, a comprehensive dataset with over half a million images capturing diverse static and dynamic hand gestures for improved recognition models.
Findings
HaGRID enables effective training of gesture recognition models.
Diversity in subjects and conditions enhances model robustness.
Pretraining on HaGRID improves recognition accuracy.
Abstract
This paper introduces an enormous dataset, HaGRID (HAnd Gesture Recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them. That is why all 18 chosen gestures are endowed with the semiotic function and can be interpreted as a specific action. Although the gestures are static, they were picked up, especially for the ability to design several dynamic gestures. It allows the trained model to recognize not only static gestures such as "like" and "stop" but also "swipes" and "drag and drop" dynamic gestures. The HaGRID contains 554,800 images and bounding box annotations with gesture labels to solve hand detection and gesture classification tasks. The low variability in context and subjects of other datasets was the reason for creating the dataset without such limitations. Utilizing crowdsourcing platforms allowed us…
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Code & Models
Videos
HaGRID — HAnd Gesture Recognition Image Dataset· youtube
Taxonomy
TopicsHand Gesture Recognition Systems · Robotics and Automated Systems
