Abstract
Hand pose estimation is a fundamental task for many human-robot interaction-related applications. In this work, we proposed novel hand pose estimation models, which leverage two types of attention mechanisms: self-attention and channel attention. By incorporating a simple yet efficient Squeeze-and-excitation (SE) block into Res152-CondPose, our best method, SERes152-CondPoseSE, successfully models interdependencies between channels. It outperforms the baseline, Res152-CondPose, by an absolute 9.56% in mean Average Precision and 17.78% in Multiple Object Tracking Accuracy.
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Notes
- 1.
Tracking performance comparison can be found at: https://youtu.be/k8ioKqLlSms.
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Nguyen, QD., Bui, AT., Nguyen, TH., Do, TH. (2023). Enhancing 2D Hand Pose Detection and Tracking in Surgical Videos by Attention Mechanism. In: Braubach, L., Jander, K., Bădică, C. (eds) Intelligent Distributed Computing XV. IDC 2022. Studies in Computational Intelligence, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-29104-3_19
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DOI: https://doi.org/10.1007/978-3-031-29104-3_19
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