  Both accuracy and efficiency are significant for pose estimation and tracking
in videos. State-of-the-art performance is dominated by two-stages top-down
methods. Despite the leading results, these methods are impractical for
real-world applications due to their separated architectures and complicated
calculation. This paper addresses the task of articulated multi-person pose
estimation and tracking towards real-time speed. An end-to-end multi-task
network (MTN) is designed to perform human detection, pose estimation, and
person re-identification (Re-ID) tasks simultaneously. To alleviate the
performance bottleneck caused by scale variation problem, a paradigm which
exploits scale-normalized image and feature pyramids (SIFP) is proposed to
boost both performance and speed. Given the results of MTN, we adopt an
occlusion-aware Re-ID feature strategy in the pose tracking module, where pose
information is utilized to infer the occlusion state to make better use of
Re-ID feature. In experiments, we demonstrate that the pose estimation and
tracking performance improves steadily utilizing SIFP through different
backbones. Using ResNet-18 and ResNet-50 as backbones, the overall pose
tracking framework achieves competitive performance with 29.4 FPS and 12.2 FPS,
respectively. Additionally, occlusion-aware Re-ID feature decreases the
identification switches by 37% in the pose tracking process.

Computer Vision and Pattern Recognition