ArticleSensors (Basel, Switzerland)2016
Human Pose Estimation from Monocular Images: A Comprehensive Survey.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Concurrent Validation of a Multi-Camera Markerless Motion Capture System Against Inertial Sensors for Upper- and Lower-Limb Joint Kinematics.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning Pose Estimation for Phenotyping of Co-Occurring Hyperkinetic Movement Disorders.Annals of clinical and translational neurology · 2026Article
- Assessment of monocular human pose estimation models for clinical movement analysis.Scientific reports · 2025Article
- A Survey of the State of the Art in Monocular 3D Human Pose Estimation: Methods, Benchmarks, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.Sensors (Basel, Switzerland) · 2025Review
- Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.Sensors (Basel, Switzerland) · 2025Article
- Sensing In Exergames for Efficacy and Motion Quality: Scoping Review of Recent Publications.JMIR serious games · 2024Article
- Home-Based, Low-Intensity, Gamification-Based, Interactive Physical-Cognitive Training for Older Adults Using the ADDIE Model: Design, Development, and Evaluation of User Experience.JMIR serious games · 2024Article
- Article
- A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation.Journal of imaging · 2023Review
- Intelligent Video Analytics for Human Action Recognition: The State of Knowledge.Sensors (Basel, Switzerland) · 2023Review
- Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2023Review
- SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time.Sensors (Basel, Switzerland) · 2022Article
- Yoga Pose Estimation and Feedback Generation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network.Sensors (Basel, Switzerland) · 2021Article
- Video-Based Analyses of Parkinson's Disease Severity: A Brief Review.Journal of Parkinson's disease · 2021Review
- Human 3D Pose Estimation with a Tilting Camera for Social Mobile Robot Interaction.Sensors (Basel, Switzerland) · 2019Article
- Drone Detection and Pose Estimation Using Relational Graph Networks.Sensors (Basel, Switzerland) · 2019Article
- Spatio-Temporal Action Detection in Untrimmed Videos by Using Multimodal Features and Region Proposals.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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7 authors.
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Abstract
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
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