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In this work, we propose a new method for multi-person pose estimation whichcombines the traditional bottom-up and the top-down methods. Specifically, weperform the network feed-forwarding in a bottom-up manner, and then parse theposes with bounding box constraints in a top-down manner. In contrast to theprevious top-down methods, our method is robust to bounding box shift andtightness. We extract features from an original image by a residual network andtrain the network to learn both the confidence maps of joints and theconnection relationships between joints. During testing, the predictedconfidence maps, the connection relationships and the bounding boxes are usedto parse the poses of all persons. The experimental results showed that ourmethod learns more accurate human poses especially in challenging situationsand gains better time performance, compared with the bottom-up and the top-downmethods.

Bottom-up Pose Estimation of Multiple Person with Bounding Box Constraint