Poa-net: dance poses and activity classification using convolutional neural networks

Abstract

Dance poses represent a complex human body-part movement, and express emotions and gesture. Dance pose classification is a challenging problem in computer vision. Convolutional Neural Networks (CNNs) have witnessed significant performance improvements in recognizing dance poses from images and videos. Most of the dance datasets in existing works are video-based and are not available publicly. This work contributes an image dataset representing 8 new dance styles blended with the Indian and international dance themes, called Dance-8. These unique 8 dance styles are combined with the Dance-12 public dataset for improving the posture diversity and dataset size. This extended dataset is called Dance-20. A custom CNN is developed for dance POses and Activity classification, named POA-Net. All three dance datasets have been evaluated using standard base CNNs and POA-Net. The POA-Net has attained an accuracy of 73.27% on Dance-8, 82.10% on Dance-12, and 73.10% on Dance-20. These performances are better than those of standard backbones, such as VGG16 and Inception-V3. The best accuracy of 81.57%, 85.08% and 76.73% has been achieved by MobileNet-v2 on these Dance-8, 12, and 20 datasets, respectively. Moreover, POA-Net has achieved the state-of-the-art accuracy of 99.74% on the DIAT, which is a radar-based human action image dataset