Egocentric Pose Estimation Using the Image Acquired by Omni-Directional Camera Attached on Chest and Cubemap

Egocentric pose estimation is the technique for estimating the positions of all the joints of the human from images obtained from the camera attached to the body. In this paper we propose a method for egocentric pose estimation using images acquired from an omni-directional camera, which is attached on the human-body chest. The method consists of two steps; In the first step, the image acquired by omni-directional camera is converted into Cubemap, which is the set of the six images which capture front, back, left, right, top and bottom of view. The six images are mapped to the sub-body-parts which correspond to arms, lower, and torso, respectively. In the second step, the pose estimation is conducted for each sub-body-part. For estimating the pose of the lower, torso and upper, the Cubemap images which are assigned to sub-body-parts are input to and processed via the trained Convolutional Neural Network respectively. Finally, the prediction from each network is marginalized for estimating the pose of full body. To confirm the validity of our method, we made the dataset by under the simulation-environment which simulates the human-model based on human motion dataset[17]. Experimental results show that ours outperforms the baseline which processes equirectangular image by pre-trained large-scale CNN in most of many joints in each coordinate.