# zhec/realtime_multi-person_pose_estimation

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5,123 stars · 1,355 forks · Jupyter Notebook · NOASSERTION

## Links

- GitHub: https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation
- awesome-repositories: https://awesome-repositories.com/repository/zhec-realtime-multi-person-pose-estimation.md

## Topics

`caffe` `computer-vision` `cpp11` `cvpr-2017` `deep-learning` `human-behavior-understanding` `human-pose-estimation` `matlab` `python` `realtime`

## Description

This is a multi-person pose estimation framework designed for real-time human keypoint detection. It functions as a bottom-up human pose estimator that identifies skeletal joints across all people in a scene without requiring a separate person detector.

The system utilizes a convolutional neural network model to generate heatmaps and vector fields for posture analysis. It specifically implements part affinity fields to encode the location and orientation of limbs, allowing the model to connect individual joints into complete skeletons.

The project covers computer vision motion analysis and deep learning posture analysis, extracting spatial coordinates of human body parts from image and video data.

## Tags

### Artificial Intelligence & ML

- [Pose Estimation](https://awesome-repositories.com/f/artificial-intelligence-ml/pose-estimation.md) — Provides a real-time system for detecting and tracking multi-person human body landmarks. ([source](https://github.com/zhec/realtime_multi-person_pose_estimation#readme))
- [Multi-Person Keypoint Localization](https://awesome-repositories.com/f/artificial-intelligence-ml/keypoint-detection/keypoint-visualizers/multi-person-keypoint-localization.md) — Simultaneously identifies and marks anatomical keypoints for multiple individuals in a scene.
- [Computer Vision Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/computer-vision-inference.md) — Implements a high-performance system for real-time execution of vision-based models to detect human poses in video streams.
- [Bottom-Up Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/pose-estimation-engines/bottom-up-pipelines.md) — Implements a bottom-up pipeline that detects keypoints globally and groups them into distinct human instances.
- [Pose Estimation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/pose-estimation-frameworks.md) — Implements a deep learning pose estimation framework to extract skeletal coordinates from images.
- [Vector Field Estimation](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-field-estimation.md) — Implements part affinity fields to encode vector fields representing the orientation and location of limbs.
- [Non-Maximum Suppression](https://awesome-repositories.com/f/artificial-intelligence-ml/bounding-box-detection/non-maximum-suppression.md) — Uses non-maximum suppression to refine raw heatmap predictions into precise joint coordinates.
- [Greedy Keypoint Grouping](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/joint-detection-embedding-architectures/greedy-keypoint-grouping.md) — Employs a greedy grouping strategy to connect individual joints into complete human skeletons.
- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Utilizes a convolutional neural network architecture to process image data for posture analysis.
- [Deep Learning Motion Tracking Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-motion-tracking-toolkits.md) — Uses deep learning toolkits to extract spatial coordinates of human body parts for skeletal tracking.
- [Heatmap-Based Joint Estimation](https://awesome-repositories.com/f/artificial-intelligence-ml/joint-embedding-predictive-architectures/heatmap-based-joint-estimation.md) — Predicts the probability of specific body joints at every pixel using heatmap-based convolutional layers.
- [Convolutional Backbones](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-training-pipelines/backbone-model-integration/convolutional-backbones.md) — Uses convolutional backbones for extracting spatial features to generate heatmaps and affinity fields.

### Part of an Awesome List

- [Human Motion Analysis](https://awesome-repositories.com/f/awesome-lists/ai/human-motion-analysis.md) — Analyzes human movement and posture in digital video to understand physical activity.
- [Pose estimation](https://awesome-repositories.com/f/awesome-lists/more/pose-estimation.md) — Listed in the “Pose estimation” section of the Ailia Models awesome list.