DeepPoseNet: A Comprehensive Study on Human Pose Estimation with Deep Learning Technique

Abstract

The article is a thorough analysis on the deep learning techniques used in image processing to estimate human poses. This entails examining several essential architectures such as CNNs and why traditional methods are unfit. It clarifies attentive mechanisms and transfer learning parts. This approach uses a two stage CNN model, whereby first network identifies some body parts, while the other focuses on these identified body bits. We use an intricate VGG16 to pinpoint body parts with accuracy. These models are compared using benchmark data sets and performance measures of special interest in the application of the MPII dataset for model training as well as verification. Deep pose estimation has huge social and economic consequences. They include human-computer interaction, sports analysis, healthcare, and many more. Conclusion gives an outline of important insights made above, highlighting positive aspects identified as well as gaps that require additional research including call towards cooperation between disciplines for enhanced growth in this field.