For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.
@article{arxiv.1905.09500,
title = {Pose estimator and tracker using temporal flow maps for limbs},
author = {Jihye Hwang and Jieun Lee and Sungheon Park and Nojun Kwak},
journal= {arXiv preprint arXiv:1905.09500},
year = {2019}
}
Comments
Won the Honorable Mention Award in the 18'ECCV PoseTrack challenge. Accepted in the 19'IJCNN conference