Introduction
A large-scale skeleton annotation project was undertaken to support advanced robotics and computer vision research focused on fine motor skill replication. The dataset comprised 4,500 assembly videos featuring electronic component assembly, each requiring detailed hand movement analysis for digital reconstruction. The resulting annotations provided the precise positional data needed to train robotic systems for delicate assembly tasks.
The Challenge
Hand joint tracking in assembly environments introduces unique difficulties that go well beyond standard video annotation.
- Fine-grained joint tracking: Each hand required annotation of 21 individual joints across thousands of frames, demanding pixel-level accuracy for every data point.
- Occlusion and visual noise: Hands were frequently hidden behind electronic components, tools, or work surfaces, and many frames suffered from motion blur during rapid assembly movements.
- Gloves and finger ambiguity: Workers wearing gloves reduced visibility of finger boundaries, and overlapping hands during two-handed operations created additional segmentation challenges.
- Component diversity: Different electronic components required different grip styles and interaction patterns, meaning annotators needed to understand the physical context of each assembly step.
The Solution
Our team implemented a structured annotation pipeline with multiple quality checkpoints to maintain accuracy at scale.
- Standardized 21-point joint mapping: A consistent skeleton annotation framework was applied across all videos, ensuring uniform data structure regardless of the assembly task or camera angle.
- Context-aware frame analysis: Annotators used surrounding frames and assembly context to accurately position joints during occlusion events, rather than guessing or skipping ambiguous frames.
- Quality-driven review loops: Multiple validation passes were built into the workflow, with senior annotators reviewing flagged frames and edge cases before final submission.
- Scalable execution: The pipeline was designed for throughput from the start, with over 7,000 annotation tasks completed on schedule across the full video set.
The Result
The project delivered a comprehensive hand tracking dataset ready for immediate use in robotics training and computer vision research.
- 7,000+ annotation tasks completed at 95% accuracy
- Precise hand and finger positional data across all 4,500 assembly videos
- Coverage of diverse electronic component scenarios, capturing the full range of grip patterns and assembly motions
- Data ready for training robotic hands to replicate fine motor assembly tasks with human-level dexterity
