mlsanityData to 7 Oct 2026

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

arXiv 2310.13039 ↗First posted 19 Oct 2023cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 237 of its 372 references that are in the map. First posted on arXiv 19 Oct 2023.

Builds on

237 of 372 references.

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38 more
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50 most cited of 237 All 237 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shotcited by 98
  2. 2026BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognitioncited by 1
  3. 2026EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusioncited by 1
  4. 20253D Human Pose and Shape Estimation from LiDAR Point Clouds: A Reviewcited by 0
  5. 2025PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimationcited by 0
  6. 2026KD-Judge: A Knowledge-Driven Automated Judge Framework for Functional Fitness Movements on Edge Devicescited by 0