Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian Classical Dance Gesture Recognition
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913353600860160 |
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| author | Raju, Kavitha Warrier, Nandini J. Madhavan, Manu C., Selvi Warrier, Arun B. Kumar, Thulasi |
| author_facet | Raju, Kavitha Warrier, Nandini J. Madhavan, Manu C., Selvi Warrier, Arun B. Kumar, Thulasi |
| contents | The classical dances from India utilize a set of hand gestures known as Mudras, serving as the foundational elements of its posture vocabulary. Identifying these mudras represents a primary task in digitizing the dance performances. With Kathakali, a dance-drama, as the focus, this work addresses mudra recognition by framing it as a 24-class classification problem and proposes a novel vector-similarity-based approach leveraging pose estimation techniques. This method obviates the need for extensive training or fine-tuning, thus mitigating the issue of limited data availability common in similar AI applications. Achieving an accuracy rate of 92%, our approach demonstrates comparable or superior performance to existing model-training-based methodologies in this domain. Notably, it remains effective even with small datasets comprising just 1 or 5 samples, albeit with a slightly diminished performance. Furthermore, our system supports processing images, videos, and real-time streams, accommodating both hand-cropped and full-body images. As part of this research, we have curated and released a publicly accessible Hasta Mudra dataset, which applies to multiple South Indian art forms including Kathakali. The implementation of the proposed method is also made available as a web application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian Classical Dance Gesture Recognition Raju, Kavitha Warrier, Nandini J. Madhavan, Manu C., Selvi Warrier, Arun B. Kumar, Thulasi Computer Vision and Pattern Recognition Computation and Language The classical dances from India utilize a set of hand gestures known as Mudras, serving as the foundational elements of its posture vocabulary. Identifying these mudras represents a primary task in digitizing the dance performances. With Kathakali, a dance-drama, as the focus, this work addresses mudra recognition by framing it as a 24-class classification problem and proposes a novel vector-similarity-based approach leveraging pose estimation techniques. This method obviates the need for extensive training or fine-tuning, thus mitigating the issue of limited data availability common in similar AI applications. Achieving an accuracy rate of 92%, our approach demonstrates comparable or superior performance to existing model-training-based methodologies in this domain. Notably, it remains effective even with small datasets comprising just 1 or 5 samples, albeit with a slightly diminished performance. Furthermore, our system supports processing images, videos, and real-time streams, accommodating both hand-cropped and full-body images. As part of this research, we have curated and released a publicly accessible Hasta Mudra dataset, which applies to multiple South Indian art forms including Kathakali. The implementation of the proposed method is also made available as a web application. |
| title | Pose2Gest: A Few-Shot Model-Free Approach Applied In South Indian Classical Dance Gesture Recognition |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2404.11205 |