Hand Keypoint Detection in Single Images using Multiview Bootstrapping (1704.07809v1)

Abstract: We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers. Finally, the reprojected triangulations are used as new labeled training data to improve the detector. We repeat this process, generating more labeled data in each iteration. We derive a result analytically relating the minimum number of views to achieve target true and false positive rates for a given detector. The method is used to train a hand keypoint detector for single images. The resulting keypoint detector runs in realtime on RGB images and has accuracy comparable to methods that use depth sensors. The single view detector, triangulated over multiple views, enables 3D markerless hand motion capture with complex object interactions.

• The paper introduces a multiview bootstrapping method that iteratively refines 2D keypoint detections using 3D triangulation to improve accuracy.
• It leverages multiview geometry to filter out noisy detections and address occlusions in complex hand-object interactions.
• Results show real-time performance with PCK metrics comparable to depth-based methods, advancing markerless 3D hand motion capture.

Hand Keypoint Detection in Single Images using Multiview Bootstrapping

The paper "Hand Keypoint Detection in Single Images using Multiview Bootstrapping" by Tomas Simon, Hanbyul Joo, Iain Matthews, and Yaser Sheikh proposes an innovative approach that improves the detection of hand keypoints in single RGB images by leveraging a multiview bootstrapping technique. This approach addresses the challenges of occlusion and the lack of large annotated hand datasets.

Method Overview

The core methodology, termed multiview bootstrapping, relies on the following iterative process:

1. Initial Detection: Employ an initial keypoint detector on multiple views to generate noisy keypoint detections.
2. 3D Triangulation: Use multiview geometry to triangulate the keypoint positions in 3D from the noisy detections and filter out outliers.
3. Reprojection for Labeling: Reproject these 3D triangulated points back to 2D views to create new labeled training data.
4. Iterative Refinement: Use the newly labeled data to retrain and improve the keypoint detector, iterating the process to progressively enhance detector accuracy.

The method mathematically derives the minimum number of views necessary to achieve desired true and false positive rates for a given detector, enabling efficient annotation of images that are prone to occlusion.

Detection Performance

The resulting hand keypoint detector operates in real-time on RGB images and achieves an accuracy comparable to depth sensor-based methods. This single-view detector, when used in a multiview setup, facilitates markerless 3D hand motion capture, including complex hand-object interactions and multiple-hand scenarios.

Evaluation and Results

Quantitative Metrics:

• Probability of Correct Keypoint (PCK): The performance is quantified using PCK, reflecting the accuracy of the keypoint detector at various distance thresholds.
• Robustness to View Angle: The detector's robustness is evaluated across different viewing angles, demonstrating improved accuracy in diverse scenarios.

Qualitative Results:

• The paper provides qualitative results showcasing the application of the method in real-world scenarios, such as musical performances and hand-object manipulations.

Implications

The paper demonstrates significant practical and theoretical advancements:

• Practical Implications:
  • Enables real-time hand keypoint detection in unconstrained environments using only RGB images.
  • Facilitates advanced human-computer interaction (HCI) applications and robustness in complex scenarios involving occlusions.
  • Allows for markerless 3D hand motion capture in varied activities, supporting detailed analysis of human hand movements.
• Theoretical Implications:
  • Introduces a robust methodology for enhancing keypoint detection accuracy through multiview bootstrapping.
  • Provides a framework for generating large, annotated datasets utilizing weakly supervised learning, advancing the field of computer vision.

Future Directions

Potential future developments in AI include:

• Enhanced Generalization: Further refinement to ensure robustness in fewer camera setups and in less controlled environments, such as multiple smartphone cameras.
• Broader Applications: Extend the methodology to other areas in computer vision where occlusion poses a significant challenge, such as face and full-body keypoint detection.
• Crowdsourced Verification: Implement crowdsourcing for frame verification to streamline the training data generation process and minimize manual intervention.

Conclusion

This paper presents a method that significantly improves the state-of-the-art in hand keypoint detection using RGB images. By utilizing multiview bootstrapping, the authors demonstrate a robust, scalable approach that addresses occlusion challenges and enhances the accuracy and applicability of hand keypoint detectors. This work lays the foundation for future research and development in markerless motion capture and keypoint detection in computer vision.