  Both the tasks of multi-person human pose estimation and pose tracking in
videos are quite challenging. Existing methods can be categorized into two
groups: top-down and bottom-up approaches. In this paper, following the
top-down approach, we aim to build a strong baseline system with three modules:
human candidate detector, single-person pose estimator and human pose tracker.
Firstly, we choose a generic object detector among state-of-the-art methods to
detect human candidates. Then, the cascaded pyramid network is used to estimate
the corresponding human pose. Finally, we use a flow-based pose tracker to
render keypoint-association across frames, i.e., assigning each human candidate
a unique and temporally-consistent id, for the multi-target pose tracking
purpose. We conduct extensive ablative experiments to validate various choices
of models and configurations. We take part in two ECCV 18 PoseTrack challenges:
pose estimation and pose tracking.
