What possibly could business entities might achieve with the key points annotation approach? 

They can identify particular characteristics or landmarks on objects in pictures or movies. 

It makes difficult tasks like pose estimation, gesture identification, facial expression recognition, and 3D reconstruction possible, in addition to high-precision object detection and tracking.

It offers a thorough comprehension of the form, alignment, motion, and spatial relationships of the objects, which can enhance computer vision models’ functionality and precision and more.

Understanding Human Body Pose Detection which is also known as Key Points Detection

Key points annotation is a technique that allows you to label specific features or landmarks on objects in images or videos. It allows for easy and flexible customization of the annotation schema, depending on the application and the domain. You can define your own key points and skeletons, and adjust the number and location of the points as needed.

So, what is Key Points Detection?

As computing power and resources continue to rise, computer vision tasks like Human Pose Estimation (HPE) and tracking are becoming more manageable. To estimate and track human poses and motions, massive computational resources and highly accurate algorithmic models are required. 

Semantic key points are identified, associated, and tracked in pose estimation. Important features on the face, such as the corners of the lips, eyes, and nose, are prime examples of this. or knees and elbows. Computer vision machine learning (ML) models enable the tracking, annotation, and estimation of movement patterns for people, animals, and vehicles through the use of pose estimation.

How to estimate a human pose via Key Points Detection?

Pose estimation refers to the process by which annotators, machine learning algorithms, models, and systems identify and track the location of a person or groups of people in an image or video by using human poses, orientation, and movement.

It is often a two-step process. To detect and estimate the position and movement of joints and other elements, a bounding box is first constructed, and then key points are utilized.

Deep learning models can identify and examine human body movements and their interactions with the surroundings in movies or photos for additional training thanks to human posture estimation. 

Sectors where Human Pose Detection or Key Points Detection works…

• Healthcare to examine, diagnose, and treat patient movements during post-injury or post-surgical rehabilitation; to examine gait for neurological and orthopedic evaluations.
• Sports and fitness to monitor and evaluate exercise form and technique to avoid injuries; to develop virtual fitness coaches for individualized training regimens.
• To facilitate gesture-based control in virtual reality (VR) applications and video games, entertainment and gaming are included.
• Retail and e-commerce to boost augmented reality (AR) shopping applications and virtual try-on experiences for apparel and accessories.
• Systems that enable users to operate technology and gadgets with gestures and body language are being developed using gestures and communication.
• Improved pedestrian recognition and avoidance in self-driving cars; improved driver monitoring systems to guarantee safety and attentiveness; and much more can be achieved with autonomous vehicles.

Conclusion:

Being one of the best data labeling & annotation service providers in the US, Data Labeler offers distinct key points annotation for human body pose detection. 

Airports and law enforcement organizations (such as the FBI to look into and update portraits) identify a match by using this model to compare calculations to other faces in their database.

Also, points can be used for face recognition tools and applications, such as smartphone apps that employ facial recognition as a filter or to determine the correct position.

Interested to know more about Data Labeler and our offerings? 

For further information contact us or request a demo today!