Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark
  Analysis
Asish Bera Mita Nasipuri Ondrej Krejcar Debotosh Bhattacharjee
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 102 sport
categories and another for 12 dance styles. Two public datasets, Yoga-82 which
contains 82 classes and Yoga-107 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
multi-scale 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.