Attention! A Lightweight 2D Hand Pose Estimation Approach

Santavas, Nicholas; Kansizoglou, Ioannis; Bampis, Loukas; Γαστεράτος, Αντώνιος

doi:10.48550/arxiv.2001.08047

Cited by 1 publication

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Hand Pose: Existing work on 2D hand pose estimation [13], [15], [37] is analogous to human pose estimation. However, we are not aware of existing deep learning-based methods evaluated on hand pose tracking.…”

Section: B Articulated Pose Estimation and Trackingmentioning

confidence: 99%

“…Simon et al [13] provide a method that helps a network improve its detection of occluded joints by training on multiple views of the same scene. Santavas et al [37] introduced a self-attention module to their estimation network, and Zimmerman et al [15] introduce a dataset for the 3D pose estimation from RGB image task. We introduce a new video dataset for multi-instance articulated hand pose tracking in the in-vivo surgical environment.…”

Section: B Articulated Pose Estimation and Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Temporally Guided Articulated Hand Pose Tracking in Surgical Videos

Louis¹,

Zhou²,

Yule³

et al. 2021

Preprint

View full text Add to dashboard Cite

Articulated hand pose tracking is an underexplored 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 invivo surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for rich tasks including skills assessment, training surgical residents, and temporal action recognition. In this work, we propose a novel hand pose estimation model, Res152-CondPose, which improves 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.

show abstract

Section: B Articulated Pose Estimation and Trackingmentioning

confidence: 99%