UniDataPro/gesture-recognition

Gesture Recognition Dataset for computer vision tasks

Dataset consists of 10,000+ videos featuring individuals demonstrating 5 distinct hand gestures: "one," "four," "small," "fist," and "me." It helps researchers study gesture recognition, especially for sign language and gesture-controlled devices. The dataset features a wide array of individuals demonstrating gestures, which allows for the analysis of differences in hand shapes, sizes, and movements among various people.

By showcasing different individuals performing the gestures, the videos enable robust training of machine learning models and deep learning techniques. - Get the data

Example of the data

Each video is recorded under optimal lighting conditions and at a high resolution, ensuring clear visibility of the hand movements. Researchers can utilize this dataset to enhance their understanding of gesture recognition applications and improve the performance of recognition methods

Frequently Asked Questions

Who can benefit from this gesture recognition dataset?

This dataset can benefit computer vision researchers, human-computer interaction teams, robotics developers, and engineers building gesture-controlled interfaces. It can be especially useful for systems that need to interpret a user’s hand movement as an input command without physical controls.

What types of gestures are represented?

The dataset contains five predefined gesture categories: one, four, small, fist, and me. These categories provide distinct hand configurations and movement patterns for supervised classification and recognition experiments.

How was the gesture recognition data collected?

The videos were collected by the UniData team through a crowdsourcing service. This approach allows recordings to be gathered from multiple participants rather than relying on a single performer or controlled studio setup.

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This dataset is particularly valuable for developing and testing recognition algorithms and classification methods in hand-gesture recognition (HGR) systems. Developers and researchers can advance their capabilities in pattern recognition and explore new recognition systems that can be applied in various fields, including human-computer interaction and virtual reality.

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