The following article serves as a comprehensive guide to the OpenPose library, focusing on real-time multi-person keypoint detection. It delves into the architecture, features, and a comparative analysis with other human pose estimation methods.
In this article, we will cover the following key points:
1. Pose Estimation in Computer Vision
2. Overview of OpenPose and its functionality
3. How to effectively utilize OpenPose for research and commercial purposes
4. Alternatives to OpenPose
5. Future prospects of OpenPose
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The video demonstration showcases the output of a pose estimation application developed using Viso Suite.
The realm of computer vision and machine learning applications increasingly relies on 2D human pose estimation for input data. This is crucial for tasks related to image recognition and AI-based video analytics, particularly in action recognition, security, sports, and more.
Human pose estimation is a rapidly evolving technology within computer vision, with significant advancements in accuracy achieved in recent years through Convolutional Neural Networks (CNNs).
### Pose Estimation with OpenPose
A human pose skeleton represents an individual’s orientation in a specific format, consisting of interconnected data points describing the pose. Each data point in the skeleton is referred to as a part coordinate or point, with relevant connections between two coordinates termed as limbs or pairs. OpenPose provides an efficient approach to pose estimation, particularly in images with crowded scenes.
### What is OpenPose?
OpenPose is a real-time multi-person human pose detection library capable of detecting human body, foot, hand, and facial keypoints in single images, with a total of 135 key points. It gained recognition by winning the COCO 2016 Keypoints Challenge for its quality and robustness in multi-person settings.
### Features of OpenPose
– Real-time 3D single-person keypoint detections
– Real-time 2D multi-person keypoint detections
– Single-person tracking for enhanced detection and visual smoothing
– Calibration toolbox for estimation of camera parameters
### How to Use OpenPose
OpenPose supports various input sources such as images, videos, webcams, Flir cameras, and custom input sources. It is compatible with Nvidia GPU, AMD GPU, and CPU computing, running on platforms like Ubuntu, Windows, Mac, and Nvidia Jetson TX2.
For commercial purposes, OpenPose requires an annual fee of USD 25000.
### How Does OpenPose Work?
The OpenPose library extracts features from images, inputting them into parallel convolutional network layers to predict confidence maps for body part detection and Part Affinity Fields (PAFs) for parts association. The model cleans these predictions to estimate and allocate human pose skeletons to individuals in the image.
### OpenPose vs. Alpha-Pose vs. Mask R-CNN
OpenPose is a renowned bottom-up approach for real-time multi-person body pose estimation, with a well-structured GitHub implementation. Alpha-Pose, on the other hand, is a top-down technique focusing on precise person detection. Mask R-CNN is popular for semantic and instance segmentation, extendable for human pose estimation.
### The Bottom Line for OpenPose
Real-time multi-person pose estimation is pivotal for enabling machines to understand human interactions. OpenPose, with its lightweight variant, is well-suited for Edge AI and on-device Edge ML Inference. To effectively develop and deploy pose estimation applications, Viso Suite offers a comprehensive solution.
### What’s Next for OpenPose?
Looking ahead, OpenPose represents a significant advancement in AI and computer vision, propelling further research and applications to redefine technology engagement.
For more insights and related articles, stay tuned for updates.
