  Articulated hand pose tracking is an under-explored problem that carries the
potential for use in an extensive number of applications, especially in the
medical domain. With a robust and accurate tracking system on in-vivo surgical
videos, the motion dynamics and movement patterns of the hands can be captured
and analyzed for many rich tasks. In this work, we propose a novel hand pose
estimation model, Res152-CondPose, which improves detection and tracking
accuracy by incorporating a hand pose prior into its pose prediction. We show
improvements over state-of-the-art methods which provide frame-wise independent
predictions, by following a temporally guided approach that effectively
leverages past predictions. Additionally, we collect the first dataset,
Surgical Hands, that provides multi-instance articulated hand pose annotations
for in-vivo videos. Our dataset contains 76 video clips from 28 publicly
available surgical videos and over 8.1k annotated hand pose instances. We
provide bounding boxes, articulated hand pose annotations, and tracking IDs to
enable multi-instance area-based and articulated tracking. When evaluated on
Surgical Hands, we show our method outperforms the state-of-the-art method
using mean Average Precision (mAP), to measure pose estimation accuracy, and
Multiple Object Tracking Accuracy (MOTA), to assess pose tracking performance.
Both the code and dataset are available at
https://github.com/MichiganCOG/Surgical_ Hands_RELEASE.
