  In this paper, we propose a novel effective light-weight framework, called
LightTrack, for online human pose tracking. The proposed framework is designed
to be generic for top-down pose tracking and is faster than existing online and
offline methods. Single-person Pose Tracking (SPT) and Visual Object Tracking
(VOT) are incorporated into one unified functioning entity, easily implemented
by a replaceable single-person pose estimation module. Our framework unifies
single-person pose tracking with multi-person identity association and sheds
first light upon bridging keypoint tracking with object tracking. We also
propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a
Re-ID module in our pose tracking system. In contrary to other Re-ID modules,
we use a graphical representation of human joints for matching. The
skeleton-based representation effectively captures human pose similarity and is
computationally inexpensive. It is robust to sudden camera shift that
introduces human drifting. To the best of our knowledge, this is the first
paper to propose an online human pose tracking framework in a top-down fashion.
The proposed framework is general enough to fit other pose estimators and
candidate matching mechanisms. Our method outperforms other online methods
while maintaining a much higher frame rate, and is very competitive with our
offline state-of-the-art. We make the code publicly available at:
https://github.com/Guanghan/lighttrack.
