  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.
