Best Human Pose Estimation Models for Mobile App in 2024
In the rapidly advancing field of digital fitness, wellness and interaction technologies, real-time human pose estimation models are becoming essential tools. These models enable innovative in-app motion tracking features, enhancing user engagement and interaction quality. This article evaluates leading models like MoveNet, PoseNet, BlazePose, YOLOv8-pose_estimation, and MLKit Pose Detection, focusing on their mobile integration ease, inference time (on mobile device), keypoint accuracy, and mobile/web compatibility.
Google's TensorFlow Models: PoseNet and MoveNet
- License: Apache 2.0
- Integration Ease: Moderate (faced a lot of problems from android SDK and uses)
- Inference Time: Fast (min 25 fps on old androids)
- Keypoint Accuracy: Good
- Mobile/Web Compatibility: High (except android 🥲)
- Body Keypoints: 17 in 2D (x, y)
Google's TensorFlow framework underpins several top pose estimation models. PoseNet, known for its ability to operate in real-time across platforms, is ideal for developers looking for a reliable, versatile solution. MoveNet, praised for its speed and precision, offers two versions—Lightning for ultra-fast performance and Thunder for higher accuracy scenarios. Great models for on-edge real-time use, but they require a lot of time to integrate.
BlazePose from Google mediapipe
- License: Apache 2.0
- Integration Ease: Moderate (can be used with the TensorFlow SDK)
- Inference Time: Moderate (10 to 40 fps base on mobile device)
- Keypoint Accuracy: Very Good
- Mobile/Web Compatibility: High (except android 🥲)
- Body Keypoints: 33 in 3D (x, y, z) or 2D (x, y)
BlazePose, another gem from Google mediapipe, provides enhanced pose estimation with up to 33 detectable keypoints. It's particularly well-suited for detailed motion tracking in apps. But not optimized enough for on-edge real-time use.
YOLOv8-pose_estimation from Ultralytics
- License: GNU General Public License v3.0
- Integration Ease: Challenging (with an active community that supports each other)
- Inference Time: Moderate (10 to 60 fps base on mobile device)
- Keypoint Accuracy: High
- Mobile/Web Compatibility: Moderate
- Body Keypoints: 17 in 3D (x, y, z) or 2D (x, y)
Ultralytics' YOLOv8-pose_estimation extends the YOLO model's capabilities to pose detection, balancing speed with high accuracy.
Visit Ultralytics for more info
ML Kit Pose Detection from Google
- License: Apache 2.0
- Integration Ease: Easy (well-documented)
- Inference Time: Bad (from 2 to 30 fps on mobile device)
- Keypoint Accuracy: Good
- Mobile/Web Compatibility: Moderate
- Body Keypoints: 32 in 3D (x, y, z)
ML Kit Pose Detection makes integrating pose estimation into mobile apps straightforward, supporting a wide range of applications from fitness tracking to interactive gaming.
Check out ML Kit Pose Detection
PoseTracker API: A Flexible solution for Real-Time Pose Estimation
- License: SaaS API
- Integration Ease: Easy (well-documented, you get it ready to use in 10 minutes)
- Inference Time: Fast (Base on optimized MoveNet model so min 30 fps)
- Keypoint Accuracy: Good
- Mobile/Web Compatibility: Very Hight
- Body Keypoints: 17 in 2D (x, y)
At Movelytics, we've developed the PoseTracker API, a versatile tool based on MoveNet with the unique ability to switch between various models such as PoseNet, BlazePose, and even YOLOv8 for pose estimation. This flexibility allows developers to experiment and choose the model that best fits their application's needs without losing months on integration.
PoseTracker is designed to enhance both the developer's and the end-user's experience by providing customized, physically adaptive interactions through advanced pose estimation technologies. Our API ensures that your application is equipped with the latest in motion tracking and analysis, offering a truly personalized user experience.
