Human Pose-based Estimation, Tracking and Action Recognition with Deep
  Learning: A Survey
Lijuan Zhou Xiang Meng Zhihuan Liu Mengqi Wu Zhimin Gao Pichao Wang
Human pose analysis has garnered significant attention within both the
research community and practical applications, owing to its expanding array of
uses, including gaming, video surveillance, sports performance analysis, and
human-computer interactions, among others. The advent of deep learning has
significantly improved the accuracy of pose capture, making pose-based
applications increasingly practical. This paper presents a comprehensive survey
of pose-based applications utilizing deep learning, encompassing pose
estimation, pose tracking, and action recognition.Pose estimation involves the
determination of human joint positions from images or image sequences. Pose
tracking is an emerging research direction aimed at generating consistent human
pose trajectories over time. Action recognition, on the other hand, targets the
identification of action types using pose estimation or tracking data. These
three tasks are intricately interconnected, with the latter often reliant on
the former. In this survey, we comprehensively review related works, spanning
from single-person pose estimation to multi-person pose estimation, from 2D
pose estimation to 3D pose estimation, from single image to video, from mining
temporal context gradually to pose tracking, and lastly from tracking to
pose-based action recognition. As a survey centered on the application of deep
learning to pose analysis, we explicitly discuss both the strengths and
limitations of existing techniques. Notably, we emphasize methodologies for
integrating these three tasks into a unified framework within video sequences.
Additionally, we explore the challenges involved and outline potential
directions for future research.