Pose Estimation in Computer Vision: Concepts & Implementation | Computer Vision

Pose Estimation is a computer vision technique that involves determining the pose of a human or any animal from an image or video. The “pose” refers to the position and orientation of an object on the coordinate system.

In this tutorial, we will discuss human pose estimation, which means the human body estimates the position of key body joints or landmarks in an image. 

There are various approaches to pose estimation, including:

2D Pose Estimation: This method estimates the 2D coordinates of key points or landmarks in an image. Common use cases include face, hand, and body tracking in 2D space. Libraries like OpenCV can be used for 2D pose estimation.

3D Pose Estimation: This technique estimates the 3D position of key points or landmarks in a scene. It's often used in robotics, augmented reality, and human-computer interaction. Depth sensors like Microsoft Kinect or stereo cameras can be used for 3D pose estimation.

Human Pose Estimation: This is a specialized application of pose estimation that focuses on estimating the pose of human bodies. It's commonly used in applications like gesture recognition, fitness tracking, and animation. There are various algorithms and deep learning models designed for human pose estimation, such as OpenPose, PoseNet, and PoseNet2.

Object Pose Estimation: This involves estimating the 3D pose of objects in a scene, which is important in robotics, autonomous vehicles, and augmented reality. Methods often involve using geometric techniques, depth data, or combination with 2D image information.

To access full documentation for specific pose estimation libraries or software, you would need to refer to their respective official documentation.

Building a 2D Pose Estimation Model

You will get the full project code on Google Colab. 

Imported necessary library 

Setup 

%pip install ultralytics
import ultralytics
ultralytics.checks()

Predict 

# Run inference on an image with YOLOv8n
!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'

# Download COCO val
import torch
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') 
 # download (780M - 5000 images)
!unzip -q tmp.zip -d datasets && rm tmp.zip  # unzip

# Validate YOLOv8n on COCO8 val
!yolo val model=yolov8n.pt data=coco8.yaml

Train 

#@title Select YOLOv8 🚀 logger {run: 'auto'}
logger = 'TensorBoard' #@param ['Comet', 'TensorBoard']
if logger == 'Comet':
  %pip install -q comet_ml
  import comet_ml; comet_ml.init()
elif logger == 'TensorBoard':
  %load_ext tensorboard
  %tensorboard --logdir .

# Train YOLOv8n on COCO8 for 3 epochs
!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640

!yolo export model=yolov8n.pt format=torchscript

from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml')  # build a new model from scratch
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
# Use the model
results = model.train(data='coco128.yaml', epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image
results = model.export(format='onnx')  # export the model to ONNX format
# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it
from ultralytics import YOLO
model = YOLO('yolov8n-pose.pt')  # load a pretrained YOLOv8n classification model
model.train(data='coco8-pose.yaml', epochs=3)  # train the model
model('https://ultralytics.com/images/bus.jpg')  # predict on an image

Practical Applications of Pose Estimation:

• Healthcare: It is used in physical therapy and monitoring patient movements. 
  
• Sports Analysis: Tracking athletes' movements for performance analysis and injury prevention
• Retail: Enhancing customer experiences through virtual try-ons and gesture-based interactions.
• Autonomous Vehicles: Monitoring driver and passenger safety and comfort
• Security: Surveillance and anomaly detection in public spaces.

Challenges in Pose Estimation:

Many challenges occur for pose estimation. There are some: 

• Occlusion: When body parts are partially or fully occluded, it becomes challenging to estimate poses accurately.
• Varying Viewpoints: Changes in camera perspective can affect the visibility of key points, making it difficult to maintain accuracy.
• Complex Poses: Estimating poses with complex configurations or extreme flexibility is a challenging problem.

In this tutorial, we try to cover basic pose estimation, types of pose estimation, and implementation of pose estimation. In a single tutorial, you cannot learn completely. For more, you can follow here.