Human pose estimation: a technology at the heart of Computer Vision
Have you ever heard about the concept of human pose estimation? In fact, the estimation of the human pose is a rather advanced technology in Computer Vision. It makes it possible to detect and analyze the position of the joints of the human body from images or videos. Models play a fundamental role in neural networks to predict the position of joints in the human body. This technology has a variety of uses, ranging from video games to health, through augmented reality, robotics and of course sport! In this article, we tried to explore the subject of human pose estimation to help you fully understand this important concept in artificial intelligence.
First, remember that human pose estimation uses sophisticated algorithms to recognize shapes and movements. These algorithms often rely on deep neural networks and machine learning. They can accurately identify the positions of different parts of the body, even under complex conditions. We'll tell you more about it below!
How does human pose estimation work?
As indicated in our introduction, human pose estimation is a technology that aims to detect and analyze the position of the joints of the human body through one or more images or videos, thus making it possible to detect a person's posture. It identifies key points in the body, such as:
. shoulders;
. elbows;
. wrists;
. hips;
. knees;
. ankles.
Then, it connects them to form a virtual skeleton. This process makes it possible to understand human postures and movements with great precision.
The way human pose estimation works is mainly based on Computer Vision and machine learning algorithms. Neural network models are used to predict the position of joints in the human body.
Here are the key steps in this process:
Image preprocessing
The image or video is first preprocessed to improve quality and extract relevant characteristics. This may include normalizing colors, filtering noise, and adjusting brightness and contrast.
Detecting key points
Algorithms identify key points in the human body. Detecting human poses in non-cooperative environments, such as occlusion and clothing variations, presents significant challenges. This can be done using convolutional neural networks (CNN). The latter are trained on annotated data sets containing images of people with markers on their joints.
Skeleton construction
Once the key points are detected, they are connected to form a virtual skeleton. This makes it possible to visualize the human pose in terms of joint segments.
Analysis of the pose
The virtual skeleton is then analyzed to understand postures and movements. Both top-down and bottom-up approaches to estimating human poses are often used to address the complexity of the human body and the challenges of detecting posture. This may include evaluating joint angles, detecting gestures, and recognizing actions.
Post-treatment
The pose data can be filtered and smoothed to correct possible errors and improve accuracy. The results can then be used in various fields, such as animation, gesture recognition, or biomechanical analysis.
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What are the areas of use of human pose estimation?
Human pose estimation has found varied applications in many fields, thanks to its ability to analyze and interpret human movements with precision. Specific techniques used in these areas include artificial intelligence, computer vision, and various machine learning methods. Here are some of the main areas of use:
Augmented reality (AR) and virtual reality (VR)
- Natural interaction : Augmented reality and virtual reality systems use human pose estimation to allow for more intuitive and immersive interactions. Users can manipulate virtual objects or navigate virtual environments through the movement of their bodies.
- Full body tracking : Advanced virtual reality applications use this technology to track the user's entire body, improving the immersive experience.
Video games
- Motion capture : Human pose estimation is used to capture player movements in real time, allowing direct interaction with the game without physical controllers.
- Improving the user experience : Video games can become more engaging and interactive by using gesture recognition to control in-game actions.
Health, fitness and sport
- Biomechanical analysis : Health professionals use this technology to analyze patients' body mechanics, helping to diagnose and treat musculoskeletal disorders.
- Posture correction : Fitness applications use pose estimation to correct posture and provide real-time feedback during exercises.
- Rehabilitation : It helps to monitor patients' progress in physical rehabilitation by analyzing their movements and adjusting treatments accordingly.
- Performance analysis : Athletes and coaches use pose estimation to analyze techniques and performances, identifying areas for improvement.
- Injury prevention : It helps monitor athletes' movements to prevent injuries by detecting risky postures.
Security and surveillance
- Detecting suspicious behavior : Surveillance systems can use pose estimation to identify abnormal or suspicious behavior in public places, helping to prevent incidents.
- Scene analysis : In industrial environments, it is used to monitor the postures of workers and to prevent accidents related to incorrect postures.
Social and interactive robots
- Human-robot interactions : Social robots use human pose estimation to understand and imitate human gestures, improving interactions between humans and robots.
- Navigation aid : Robots can use this technology to avoid collisions with humans by detecting and interpreting their movements.
Arts and entertainment
- Animation and cinema : Animation studios use pose estimation to capture actors' movements and transfer them to virtual characters.
- Live performances : It allows real-time special effects and innovative interactions in artistic shows and performances.
💡 Human pose estimation is a versatile technology that continues to evolve, and offers new possibilities in a large number of sectors thanks to its advanced capabilities to analyze and interpret human movements.
Conclusion
Human pose estimation is an innovative technology that is revolutionizing the way computers interact with the physical world. It opens up new perspectives in many sectors, and its adoption can lead to significant innovations and enriched user experiences.
Despite certain advances, human pose estimation still has limits in terms of precision, robustness and real-time processing. There are various types of approaches for estimating human pose, including top-down methods (Top Down) and ascending (Bottom-up), each with its pros and cons. However, rapid advances in artificial intelligence promise continuous improvements in the near future, opening the door to new possibilities and diverse applications.
