Explore About Human Pose Estimation

Topology:

The current standard for human body pose is the COCO topology, which consists of 17 landmarks across the arms, legs, and face. However, the COCO keypoints only localize to the ankle and wrist points, lacking scale and orientation information for hands and feet, which is vital for practical applications like fitness and dance. The inclusion of more keypoints is crucial for the subsequent application of domain-specific pose estimation models, like those for hands, face, or feet.

Outlook And Trends:

Pose estimation for objects is a major trend in computer vision. Object pose estimation allows gaining a more detailed understanding of objects compared to two-dimensional bounding boxes. Until now, pose estimation is still computationally very intensive and requires expensive AI hardware (often multiple NVIDIA GPUs) that is not practical for real-world use.
Edge AI technology New technologies and methods make it possible to decrease the size of AI models, making pose estimation algorithms less “heavy” and much more efficient. This is the basis for the real-world implementation of human pose detection. As a result, it becomes possible to deploy pose estimation algorithms to edge devices and perform on-device machine learning (Edge AI). Edge Inference makes the technology scalable, more robust for mission-critical applications (offline capability), and private (no visuals need to be sent to the cloud). An example of a lightweight pose estimation model for Edge ML is Lightweight OpenPose.

The Future Scope:

1. Multi-Person Pose Estimation
• Extend the current model to handle multiple people in the frame simultaneously. This involves detecting and estimating the poses of all individuals present in a real-time video stream.
2. 3D Pose Estimation
• Enhance the model to estimate the three-dimensional poses of humans. This can be valuable in applications like augmented reality, virtual reality, and robotics, where 3D pose information is crucial.
3. Real-Time Gesture Recognition
• Integrate gesture recognition capabilities into the system. This would enable the model to detect and recognize specific human gestures in real-time, opening up applications in sign language interpretation, human-computer interaction, and virtual reality gaming.
4. Real-Time Pose Estimation on Edge Devices
• Optimize the model to run efficiently on edge devices like smartphones, cameras, and other embedded systems, enabling real-time pose estimation without the need for extensive computational resources.
5. Privacy and Security Considerations
• Address privacy concerns related to pose estimation and ensure that the system does not compromise individuals' privacy or security.
6. Real-Time Feedback and Coaching
• Develop interactive applications that provide real-time feedback and coaching based on the detected poses. This can be useful in fitness and rehabilitation scenarios.
7. Collaborative Pose Estimation
• Investigate methods for collaborative pose estimation, where multiple devices or cameras work together to estimate poses accurately in a shared environment.
8. Self-Supervised Learning
• Explore self-supervised learning techniques for pose estimation, reducing the dependency on large annotated datasets.
9. Application in Virtual Try-On and Fashion
• Utilize pose estimation for virtual try-on applications, allowing users to visualize clothing and accessories on their bodies in real-time.

Applications Of Human Pose Estimation:

One of the clearest areas in which pose estimation is applicable is in tracking and measuring human movement. Using human pose estimation to track human movement could also power several other experiences such as:

1. AI Powered Sports Coaches And Personal Trainers
• Zenia is an AI-powered yoga app that uses Human Pose Estimation to guide you towards achieving a proper posture during your yoga workouts. It uses the camera to detect your pose and estimates how accurate your pose is-if it is correct, then the predicted pose will be represented in green. If the pose isn't correct, the red color will replace the green one.
2. Surveillance And Human Activity Monitoring
• Pose estimation is highly valued in surveillance systems in the big data. Some surveillance cases don’t require spotting a crime in a crowd of people. Instead, cameras can be used to automate everyday processes like shopping at a grocery store. Cashierless store systems like Amazon GO, for example, apply human pose estimation to understand whether a person took some item from a shelf
3. Animation And Gaming
• Game development is a tough industry with a lot of complex tasks that require knowledge of human body. Human pose estimation is widely used in animation of game characters to simplify this process by transferring tracked key points in a certain position to the animated model.
4. Athlete Pose Detection
• Pose detection can help players to improve their technique and achieve better results. Apart from that, pose detection can be used to analyze and learn about the strength and weaknesses of the opponent, which is invaluable for professional athletes and their trainers.
5. Robotics
• Robotics has been one of the fastest-growing areas of development. While programming a robot to follow a procedure can be tedious and time-consuming, deep learning approaches can come to the rescue. Techniques such as reinforcement learning use a simulated environment to achieve the accuracy level required to perform a certain task and can be successfully used to train a robot.