New keypoint-based approach for recognising British Sign Language (BSL) from sequences
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Abstract (arXiv)
In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational efficiency and memory usage. Furthermore, it offers expedited training times and demands fewer computational resources. To the best of our knowledge, this is the inaugural application of a keypoint-based model for BSL word classification, rendering direct comparisons with existing works unavailable.
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References · 9
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
- 2018DensePose: Dense Human Pose Estimation In The Wildcited by 573
- 2019MediaPipe: A Framework for Building Perception Pipelinescited by 572
- 2020BSL-1K: Scaling up co-articulated sign language recognition using mouthing cuescited by 106
- 2015Flowing ConvNets for Human Pose Estimation in Videoscited by 55
- 2019Spatial-Temporal Graph Convolutional Networks for Sign Language Recognitioncited by 23
- 2021A Two-stream Neural Network for Pose-based Hand Gesture Recognitioncited by 10
- 2016Real-time Human Pose Estimation from Video with Convolutional Neural Networkscited by 2
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