OpenPose is a real-time multi-person keypoint detection library developed by the CMU Perceptual Computing Lab. It is designed to estimate keypoints for the body, face, hands, and feet from images and videos.
Key Features
- Multi-Person Detection: OpenPose can detect multiple people in an image or video, providing keypoint estimations for each individual.
- Comprehensive Keypoint Detection: It supports the detection of keypoints for the entire body, face, hands, and feet, making it a versatile tool for various applications in computer vision and human-computer interaction.
- Real-Time Performance: The library is optimized for real-time performance, allowing for live keypoint detection from webcams or video streams.
Usage
OpenPose can be used on various platforms, including Windows, Ubuntu, and Mac. Users can either download the portable binaries for quick setup or compile the code from source for more customization.
Running the Demo
To run the OpenPose demo, follow these steps:
-
Ubuntu and Mac:
bash
./build/examples/openpose/openpose.bin --video examples/media/video.avi -
Windows:
bash
bin\OpenPoseDemo.exe --video examples/media/video.avi
Output Formats
OpenPose provides various output formats, including JSON, images, and video. The JSON output includes arrays of keypoints with their coordinates and confidence scores. The keypoints can be normalized or scaled according to user preferences [1][2][3].
Advanced Configuration
OpenPose offers numerous flags for advanced configuration, allowing users to adjust parameters such as output resolution, network resolution, and multi-threading options. These configurations help in balancing between accuracy and performance based on the specific requirements of the application [3].
Installation
For those who want to modify or integrate OpenPose with other projects, the library can be built using CMake-GUI. The detailed installation instructions are available in the OpenPose documentation, which guides users through the process of setting up the necessary dependencies and compiling the code [5].
Conclusion
OpenPose is a powerful and flexible library for real-time multi-person keypoint detection. Its ability to provide detailed keypoint estimations for various parts of the human body makes it an invaluable tool for researchers and developers in the field of computer vision.
Sources:
– [1] GitHub – OpenPose Documentation
– [2] CMU-Perceptual-Computing-Lab/openpose – GitHub
– [3] OpenPose Advanced Doc – Demo – GitHub Pages
– [5] OpenPose Doc – Installation – GitHub Pages
Further Reading
1. openpose/doc/01_demo.md at master · CMU-Perceptual-Computing-Lab/openpose · GitHub
2. Activity · CMU-Perceptual-Computing-Lab/openpose · GitHub
3. OpenPose: OpenPose Advanced Doc – Demo – Advanced
4. Dean/openpose | DagsHub
5. OpenPose: OpenPose Doc – Installation