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.

该论文综述了基于深度学习的姿势分析应用，包括姿势估计、姿势跟踪和动作识别，并讨论了现有技术的优势和局限性，重点介绍了将这三个任务整合到视频序列的统一框架中的方法，并探讨了相关挑战和未来研究方向。

基于人体姿势的深度学习估计、跟踪和动作识别：综述

Human Pose-based Estimation, Tracking and Action Recognition with Deep  Learning: A Survey

Human pose analysis is presently dominated by deep convolutional networks
trained with extensive manual annotations of joint locations and beyond. To
avoid the need for expensive labeling, we exploit spatiotemporal relations in
training videos for self-supervised learning of pose embeddings. The key idea
is to combine temporal ordering and spatial placement estimation as auxiliary
tasks for learning pose similarities in a Siamese convolutional network. Since
the self-supervised sampling of both tasks from natural videos can result in
ambiguous and incorrect training labels, our method employs a curriculum
learning idea that starts training with the most reliable data samples and
gradually increases the difficulty. To further refine the training process we
mine repetitive poses in individual videos which provide reliable labels while
removing inconsistencies. Our pose embeddings capture visual characteristics of
human pose that can boost existing supervised representations in human pose
estimation and retrieval. We report quantitative and qualitative results on
these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.

通过自我监督学习利用时空关系进行姿态嵌入的深度卷积网络，包括时序和空间任务，在数据采样中利用一种课程学习方法，并挖掘单个视频中的重复姿态，以提高姿势估计和检索效果。