3D human pose estimation and action recognition using fisheye cameras: A survey and benchmark

3D human pose estimation based on visual information aims to predict 3D poses of humans in images or videos. The aim of human action recognition is to classify what kind of actions people do. Both topics are widely studied in the field of computer vision. Existing methods mainly focus on 3D human pose estimation and human action recognition using images/videos recorded by perspective cameras. In contrast to perspective cameras, fisheye cameras use wide-angle lenses capturing wider field-of-views (FOV). Fisheye cameras are used in many applications such as surveillance and autonomous driving. In this paper, a survey is given on monocular 3D human pose estimation and action recognition. A new benchmark dataset is proposed using a fisheye camera to quantitatively compare and analyse existing methods. • We summarize existing methods for 3D human pose estimation (HPE) and human action recognition (HAR) from pin-hole and fisheye cameras. • We propose a new dataset taken by fisheye cameras for 3D HPE and HAR. • We compare and analyze the existing methods on public and our datasets. • We outline the challenge and future perspectives.