Effectual pre-processing with quantization error elimination in pose detector with the aid of image-guided progressive graph convolution network (IGP-GCN) for multi-person pose estimation

Multi-person pose estimation (MPE) remains a significant and intricate issue in computer vision. This is considered the human skeleton joint identification issue and resolved by the joint heat map regression network lately. Learning robust and discriminative feature maps is essential for attaining precise pose estimation. Even though the present methodologies established vital progression via feature map's interlayer fusion and intralevel fusion, some studies show consideration for the combination of these two methodologies. This study focuses upon three phases of pre-processing stages like occlusion elimination, suppression strategy, and heat map methodology to lessen noise within the database. Subsequent to pre-processing errors will be eliminated by employing the quantization phase by embracing the pose detector. Lastly, Image-Guided Progressive Graph Convolution Network (IGP-GCN) has been built for MPE. This IGP-GCN consistently learns rich fundamental spatial information by merging features inside the layers. In order to enhance high-level semantic information and reuse low-level spatial information for correct keypoint representation, this also provides hierarchical connections across feature maps of the same resolution for interlayer fusion. Furthermore, a missing connection between the output high level information and low-level information was noticed. For resolving the issue, the effectual shuffled attention mechanism has been proffered. This shuffle intends to support the cross-channel data interchange between pyramid feature maps, whereas attention creates a trade-off between the high level and low-level representations of output features. This proffered methodology can be called Occlusion Removed_Image Guided Progressive Graph Convolution Network (OccRem_IGP-GCN), and, thus, this can be correlated with the other advanced methodologies. The experimental outcomes exhibit that the OccRem_IGP-GCN methodology attains 98% of accuracy, 93% of sensitivity, 92% of specificity, 88% of f1-score, 42% of relative absolute error, and 30% of mean absolute error.