*Articulated Human Pose Estimation Using Greedy Approach DOI: http://dx.doi.org/10.5772/intechopen.99354*

architecture with two parallel branches one of the branches estimates the body joints via hotspots while the other branch captures the orientations of the joints through vectors. We ran our model on a publicly available COCO dataset for training, cross validation and testing. Finally, we evaluated the results and achieved a mean average precision of 77.7. We compare our results with existing models and achieved and a significant rise of 2.5% in mAP with less inference time. We have showed the results in **Tables 1 and 2**. We aim to expand our project in future by proposing a framework for human pose comparator based on the underlying technology used in single person pose estimation to compare the detected pose with that of the target in real-time. This would be done by developing a model to act as an activity evaluator by learning physical moves using key points detection performed by the source and compare the results with the moves performed by the target along with a scoring mechanism that would decide how well the two sequence of poses match. In a nutshell, we aim to build an efficient comparison mechanism that would accurately generate the similarity scores based on the series of poses between the source and the target as the future scope of this project.
