Project Introduction: Lightweight Human Pose Estimation
The "Lightweight Human Pose Estimation" project offers a simplified, yet efficient method for real-time 2D multi-person pose estimation, specifically optimized for CPU use. The initiative is an adaptation of the well-known OpenPose framework, achieving nearly the same accuracy but with significantly reduced computational requirements. This makes it feasible to perform pose detection even on less powerful devices.
Key Features
The project focuses on detecting human skeletons, which consist of keypoints and the connections between them. These include 18 keypoints such as the ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. The system achieves an Average Precision (AP) score of 40% on the COCO 2017 Keypoint Detection validation set with single scale inference, without any flipping or additional post-processing steps.
Requirements and Setup
To run this system, you need Ubuntu 16.04, Python 3.6, and PyTorch 0.4.1. The setup process requires downloading the COCO 2017 dataset and installing additional Python libraries specified in a requirements file. This groundwork enables you to prepare the environment for both training and validation stages.
Training Process
The training comprises three incremental steps, each enhancing the model's precision. Initially, training begins with weights from MobileNet, subsequently refining these weights in successive steps. By the final stage, the model includes three refinement stages, pushing the AP score to around 40%.
Validation and Pre-trained Models
Validation of the model involves assessing it on a set of validation images to ensure its performance aligns with expectations. A pre-trained model is available for developers to ascertain immediate results or build upon it. The model operates on images processed to a specific format, ensuring efficiency and consistency in various computational environments.
Demos and Practical Implementations
Several demos, both in C++ and Python, enable users to witness the model in action. While Python demos offer quick and easy testing directly from webcams, the C++ versions provide optimized performance suitable for deployment.
Advanced Conversions and Implementations
The project includes scripts to convert the model into ONNX and OpenVINO formats, allowing broad applications and integration with Intel’s toolkit for further performance enhancements. These conversions ensure the model can be utilized across different platforms and architectures, maintaining its lightweight and efficient nature.
Community and Further Developments
The project acknowledges its community and directs users to newer works focusing on single-person pose estimation and 3D pose detection models. These newer models adhere to the project's ethos of lightweight and accurate computation, offering more precise solutions while retaining the speed and efficiency necessary for real-time applications.
For users or researchers who find this project beneficial, the creators encourage citation of their work, fostering a collaborative and credit-sharing environment within the field of computational pose estimation.