Hand Keypoint Detection Dataset

Context: Hand detection is crucial in many applications, including gesture recognition, sign language interpretation, and human-computer interaction. This dataset meets the need for accurate and robust detection models by offering a rich set of annotated hand images from various perspectives. Additionally, this comprehensive collection of images helps improve model performance by providing diverse and detailed examples.

Content:

• Images: The dataset includes high-resolution images featuring a wide variety of hand poses and orientations.
• Annotations: The dataset includes detailed keypoint annotations for each hand image, including finger joints and palm keypoints. This detailed annotation allows for exact analysis and application in various machine learning models and computer vision tasks.
• Metadata: Includes information on lighting conditions, background variations, and occlusions to support robust model training.

Acknowledgements: We extend our gratitude to the contributors and annotators who made this dataset possible. Special thanks to the researchers and institutions that supported this project.

Additional Content:

• Sample Code: Example scripts for loading, visualizing, and preprocessing the dataset for machine learning models.
• Evaluation Metrics: Guidelines on evaluating hand detection models using this dataset, including common metrics like Mean Average Precision (mAP).
• Use Cases: Detailed descriptions of potential applications in AR/VR, robotics, and gaming industries.

Hand Keypoint Detection in Single Images: By leveraging multiview bootstrapping, this dataset ensures comprehensive coverage of hand poses, facilitating the creation of models capable of accurate real-time hand detection.