Fundamentos de Pose Estimation con Mediapipe

The detection of poses and minutiae of the human body using artificial intelligence has revolutionized multiple industries, from retail to medicine. MediaPipe, developed by Google, offers powerful and accessible solutions to implement these technologies in real projects. In this content, we will explore how to use MediaPipe to create heat maps to analyze customer behavior for specific products.

What is MediaPipe and why is it important for pose detection?

MediaPipe is an open source tool developed by Google that offers several solutions for image and video analysis. Its main features include:

• Highly accuratefacial point detection.
• Real-timebody pose recognition.
• High precision in the detection of movements.
• Large community of developers who constantly contribute.
• Official Google support, which guarantees continuous updates and improvements.

This library can identify up to 33 characteristic points on the human body, ranging from head to toe, including shoulders, elbows, hands, knees and ankles. In addition, it offers the ability to detect more than 400 points on the face alone, making it an extremely detailed tool for body analysis.

How to implement pose detection with MediaPipe?

To start using MediaPipe in our projects, we need to have some dependencies installed:

1. OpenCV for image and video processing.
2. MediaPipe, which is easily installed with the command:

pip install mediapipe

The basic implementation to detect poses follows these steps:

import cv2import mediapipe as mp
 # Initialize MediaPipe componentsmp_drawing = mp.solutions.drawing_utilsmp_pose = mp.solutions.pose
 # Set up pose detection = mp_pose.Pose(  min_detection_confidence=0.5,  min_tracking_confidence=0.5, static_image_mode=False)
 # Capture video (0 for built-in camera, 1 for external camera)cap = cv2.VideoCapture(1)
while cap.isOpened(): success, frame = cap.read() if not success: break        
 # Convert from BGR to RGB (MediaPipe requires RGB) frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)    
 # Process frame to detect poses results = pose.process(frame_rgb)    
 # Draw landmarks and connections if results.pose_landmarks: mp_drawing.draw_landmarks( frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS )    
 # Show the result cv2.imshow('MediaPipe Pose', frame)    
 # Exit with key 'q' if cv2.waitKey(1) & 0xFF == ord('q'): break
 # Release resourcescap.release()cv2.destroyAllWindows()

This code configures MediaPipe to detect poses with a 50% confidence threshold for both initial detection and follow-up. The static_image_mode=False parameter optimizes performance for video, improving latency.

What are landmarks and how are they used in body analysis?

Landmarks are specific coordinates that MediaPipe identifies on the human body. Each point has a precise location and represents a relevant joint or anatomical feature.

In the case of the pose solution, MediaPipe identifies 33 landmarks distributed throughout the body:

• Points on the head and face
• Shoulders, elbows and wrists
• Fingers
• Hips, knees and ankles
• Feet

These points are connected by lines (represented as POSE_CONNECTIONS in the code) to form a virtual skeleton that is superimposed on the image of the detected person.

The importance of these landmarks lies in the fact that they allow:

• Analyze body posture
• Detect specific movements
• Create heat maps based on the position of parts of the body.
• Track the behavior of a person in front of stimuli (such as products in a store).

How to create heat maps to analyze customer behavior?

For the specific challenge of creating heat maps that show what attracts people to certain products, we need to focus on specific characteristic points. Although in this case we will focus on face landmarks in the next implementation, the basic concept involves:

1. Detecting the pose of the person using MediaPipe.
2. Extracting the coordinates of the relevant landmarks (such as gaze direction)
3. Recording this data over time
4. Generating a heat map showing the areas of greatest interest.

This approach allows retailers to

• Identify which products attract the most visual attention.
• Analyze movement patterns in front of shelves or displays
• Optimize product layout based on objective data
• Improve the customer experience through more intuitive layouts

The full implementation of facial point-based heat mapping will be addressed in the next phase of the project, where we will focus specifically on tracking facial features to determine where the customer is looking.

MediaPipe pose detection technology offers fascinating possibilities for analyzing human behavior in commercial environments. Have you ever implemented eye-tracking systems in your projects? Share your experience and questions in the comments.