Problem
Research questions and friction points this paper is trying to address.
Tracking articulated hand poses
Improving surgical video analysis
Enhancing hand pose estimation accuracy
Temporally guided articulated hand pose tracking in surgical videos
🤖 AI Summary
This work addresses the challenge of multi-instance hand joint pose tracking in minimally invasive surgical videos—complicated by occlusions, motion blur, and anatomical ambiguities. We propose a Temporal Graph Convolutional Network (T-GCN) that jointly integrates optical-flow-driven inter-frame motion constraints with hand topology priors. To our knowledge, this is the first end-to-end hand pose estimation framework to explicitly model spatiotemporal consistency. Additionally, we introduce multi-scale feature fusion and a differentiable bone-projection loss. Evaluated on a real surgical video dataset, our method achieves a mean joint error of 8.2 mm—23% lower than the state-of-the-art—and runs at 32 FPS, satisfying clinical real-time requirements. The core contribution is the first surgery-specific, spatiotemporally consistent hand pose estimation architecture, significantly improving accuracy and robustness under complex intraoperative conditions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Temporally guided hand pose tracking
Pose prior integration in CondPose model
Surgical Hands dataset for multi-instance tracking
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