The authors approached the task by defining the following problems: For a more thorough explanation of the algorithm, you may refer to their paper and to this blog post.ĭeepCut is a bottom-up approach for multi-person human pose estimation. Through the above steps, human pose skeletons can be estimated and assigned to every person in the image.
Using the PAF values, weaker links in the bipartite graphs are pruned. Using the part confidence maps, bipartite graphs are formed between pairs of parts (as shown in the above image). Successive stages are used to refine the predictions made by each branch. Steps involved in human pose estimation using OpenPose. Shown below is the architecture of the OpenPose model. OpenPose is one of the most popular bottom-up approaches for multi-person human pose estimation, partly because of their well documented GitHub implementation.Īs with many bottom-up approaches, OpenPose first detects parts (key points) belonging to every person in the image, followed by assigning parts to distinct individuals. In the next section, we will review some of the popular top-down and bottom-up approaches for the same.
In this blog, we will focus on multi-person human pose estimation using deep learning techniques. It is hard to judge which approach has better performance overall as it really comes down to which among the person detector and associating/grouping algorithms is better. Typically, the top-down approach is easier to implement than the bottom-up approach as adding a person detector is much simpler than adding associating/grouping algorithms.
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