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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

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dc.contributor.author Bera, Asish
dc.date.accessioned 2024-10-18T10:54:02Z
dc.date.available 2024-10-18T10:54:02Z
dc.date.issued 2023-07
dc.identifier.uri https://ieeexplore.ieee.org/abstract/document/10177209
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16132
dc.description.abstract Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the sports, yoga, and dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification (FGIC) task due to the complex movement of body parts. Deep convolutional neural networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep-learning techniques, fine-grained sports and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient interclass and intraclass variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 that contains 82 classes and Yoga-107 that represents 107 classes, are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multiscale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://sites.google.com/view/syd-net/home en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Computer Science en_US
dc.subject Attention en_US
dc.subject Convolutional neural networks (CNNs) en_US
dc.subject Posture recognition en_US
dc.subject Sports en_US
dc.subject Yoga en_US
dc.title Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis en_US
dc.type Article en_US


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