Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis
Asish Bera, Mita Nasipuri, Ondrej Krejcar, and Debotosh Bhattacharjee

TL;DR
This paper introduces new datasets and a deep learning model, SYD-Net, for fine-grained recognition of sports, yoga, and dance postures, achieving state-of-the-art accuracy and addressing a significant gap in benchmark datasets.
Contribution
The paper presents four new SYD datasets and a novel attention-based deep model, SYD-Net, for improved classification of complex sports, yoga, and dance postures.
Findings
SYD-Net achieves state-of-the-art accuracy on Yoga-82.
Proposed datasets fill a gap in fine-grained posture recognition.
Attention mechanism enhances feature discrimination.
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 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 inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for…
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Taxonomy
MethodsRandom Erasing · Balanced Selection
