Multi-Person Pose Estimation With Human Detection: A Parallel Approach

Human pose estimation is a fundamental research topic in computer vision. This topic has been largely improved recently thanks to the development of convolution neural network. This paper proposes a new CNN architecture which combines a key-points estimator and an object detector. This network can detect poses of all people and the around objects in the image in parallel. In general, to address the multi-person pose estimation, the network generates human key-points and bounding boxes simultaneously. Thus, it ensembles these key-points into full poses of multiple people based on the bounding boxes.