YOLOv7 Pose vs Mediapipe: The Battle of Human Pose Estimation Models

Human pose estimation is a fascinating and practical domain within the field of computer vision, with applications ranging from fitness coaching and healthcare to sports analytics and interactive gaming. Among the plethora of tools available for this purpose, two have gained significant traction in recent times: YOLOv7 Pose and Mediapipe. Both promise to transform human pose estimation tasks, but which one truly reigns supreme? In this detailed article, we break down these two models, covering their architectures, advantages, drawbacks, and real-world performance to help you select the most suitable solution for your next project.

Understanding Human Pose Estimation

An Overview

Human pose estimation refers to the process of predicting the locations of a person’s major joints, such as elbows, knees, and shoulders, from a still image or video. Essentially, it is a keypoint detection problem, where each joint is a specific “keypoint.” These keypoints are then interconnected based on a predefined skeleton topology to represent a person’s pose.

Keypoint detection is foundational to unlocking human motion in real-time, with the number of keypoints varying across datasets and applications:

• COCO Dataset: 17 keypoints (e.g., hips, head, ankles)
• MPII Dataset: 16 keypoints
• AI Challenger & CrowdPose Datasets: 14 keypoints

Applications of Pose Estimation

Pose estimation has countless applications, including:

• AI Fitness Trackers (e.g., Peloton systems)
• Sports Analytics – analyzing athlete movements
• Healthcare & Rehabilitation – aiding in physiotherapy and mobility analysis
• Gaming & Virtual Reality (VR) – enabling natural interactions within immersive environments
• Surveillance & Activity Recognition – tracking behavior through cameras for security systems

Given its versatility, there’s been extensive ongoing research to develop faster and more accurate human pose estimation models. Among these models, YOLO-based approaches and Mediapipe have emerged as prominent contenders.

What is YOLOv7 Pose?

Background

YOLO (You Only Look Once) is one of the most renowned object detection frameworks, known for its real-time processing speed and high accuracy. YOLOv7 Pose builds upon this legacy to perform human pose estimation in a revolutionary manner, combining both speed and accuracy. The "v7" denotes the version of YOLO upon which it is implemented.

How Does YOLOv7 Pose Work?

YOLOv7 Pose adopts an innovative end-to-end architecture that directly predicts both:

• Bounding Boxes: The region around each person
• Keypoints: Location of important joints such as shoulders, knees, etc.

This model eschews traditional two-phase architectures in favor of a streamlined single-step inference. The main highlights of YOLOv7 Pose’s design include:

1. Departure from Traditional Approaches:

• Top-Down Approach: Detects humans first, then identifies keypoints. While accurate, it is computationally expensive when multiple people are present.
• Bottom-Up Approach: Detects keypoints for the entire image and subsequently groups them into individuals. Although faster, it can struggle with crowded or complex scenes.
• YOLOv7 Pose’s Novel Approach: Combines detection and pose estimation in one step for efficient and precise keypoint localization.

2. Object Keypoint Similarity (OKS) Metric:
   YOLOv7 Pose directly optimizes OKS, enabling simultaneous bounding-box detection and keypoint estimation. This results in better coordination between the bounding boxes and their associated keypoints.

3. Customizable Backbone Architecture:
   YOLOv7 Pose is versatile and can incorporate any object detection backbone, though YOLOv7 itself forms the standard backbone for this implementation.

Key Features

• Scalability: Designed for multi-person pose estimation.
• Input Resolution: Default input size of 960P, which enables high-resolution keypoint detection.
• Speed: Balances accuracy and speed effectively for real-time applications.

What is Mediapipe and BlazePose?

Overview

Mediapipe by Google is a general-purpose framework for machine learning pipelines, encompassing solutions like face detection, hand tracking, and more. For human pose estimation, Mediapipe incorporates the BlazePose model. BlazePose is a lightweight, real-time pose estimation system optimized to run on edge devices, such as mobile phones or CPUs, without relying on high-performance GPUs.

How Mediapipe’s BlazePose Works

1. Single-Person Detection:
   Mediapipe is inherently designed for single-person tracking.

2. Detection + Tracking Framework:
   Unlike YOLOv7 Pose, Mediapipe employs a two-step mechanism:

• Detection Frame: Detects the person’s bounding box in an initial frame.
• Tracking Frames: Tracks the identified person in subsequent frames, vastly improving speed while reducing jitter.

3. Keypoint Topology:
   BlazePose provides 33 keypoints, significantly more than YOLOv7 Pose’s 17, offering a more detailed skeletal representation of the body. These include advanced keypoints like those for the face and fingertips.

4. Run-time Optimization:
   Mediapipe is heavily optimized for edge devices and CPUs, prioritizing power efficiency and real-time processing for environments lacking powerful GPUs.

Head-to-Head Comparisons of YOLOv7 Pose and Mediapipe

1. Multi-Person Capability

• YOLOv7 Pose: Naturally designed for multi-person scenarios, making it suitable for crowded scenes.
• Mediapipe: Limited to single-person pose estimation.

2. Keypoint Density

• YOLOv7 Pose: 17 keypoints based on COCO topology.
• Mediapipe: 33 keypoints, offering higher granularity, especially for hands and facial orientation.

3. Input Resolution

• YOLOv7 Pose: Default input size of 960P, making it suitable for high-quality data sources.
• Mediapipe: Optimized for 256×256 resolution (smaller input size leads to faster processing).

4. Speed and Latency

• Mediapipe’s hybrid detection-and-tracking mechanism is faster in real-time scenarios, achieving high frame rates even on edge devices, whereas:
• YOLOv7 Pose offers competitive speed on GPUs but falls behind on CPUs, especially when using larger input sizes.

5. Stability and Jitter

• Mediapipe: Tracks poses across frames, offering stable outputs with minimal flickering.
• YOLOv7 Pose: Flickers more due to its single-frame inference nature.

6. Challenging Scenarios

| Scenario | Winner | Explanation |
|———————|—————|—————————————————————|
| Occlusion | YOLOv7 Pose | Handles occluded body parts better by leveraging bounding-box and keypoint synergy. |
| Low Light | YOLOv7 Pose | Performs better in poor lighting scenarios. |
| Fast Motion | Mediapipe | Tracks person orientation even in tricky poses like upside down. |

Real-World Applications of Each Model

Use Cases for YOLOv7 Pose:

• Scenarios requiring multi-person tracking (e.g., sports events, public surveillance).
• Applications where a GPU-powered system is available.
• Environments with moderate lighting and better-quality input images.

Use Cases for Mediapipe:

• Edge devices like mobile phones or embedded systems.
• Single-person focus, such as fitness apps and healthcare.
• Real-time applications with constraints on energy consumption.

Conclusion: Choosing the Right Model

In the battle between YOLOv7 Pose and Mediapipe, the clear choice depends on your requirements:

• Choose YOLOv7 Pose if your setup involves a GPU, requires multi-person capability, and you prioritize accuracy in diverse lighting or occluded conditions.
• Choose Mediapipe if you’re working on resource-constrained devices, need real-time performance on a CPU, or require minimal jitter and stable keypoint tracking.

While both models excel in their own domains, the evolution of human pose estimation will likely bring even more powerful solutions in the future. Until then, let your project requirements guide your choice—whether it’s YOLOv7 Pose’s advanced architecture or Mediapipe’s real-time efficiency for single-person tracking.

Feel free to share your thoughts and favorite use cases for pose estimation! 🌟