A Human Pose Skeleton represents the orientation of a person in a graphical format. Essentially, it is a set of coordinates that can be connected to describe the pose of the person. Each co-ordinate in the skeleton is known as a part (or a joint, or a keypoint).
Pose estimation has garnered immense attention in the field of computer vision. The increasing interest is the ability to use computer vision techniques to identify and track the movement of a person or an object in real-time, which offers a lot of usefulness across industries. In the ever-evolving era of advanced technologies, pose estimation can become an effective tool in sports bio mechanics, animation, gaming, robotics, medical rehabilitation, and surveillance.
Essentially, pose estimation predicts different poses based on a person’s body parts and joint positioning in an image or video. For instance, we can automatically detect the joints, arms, hips, and spine position while performing a squat.
| Football Match Pose-Estimation | Cricket Match Pose-Estimation | FPS and Time Comparision | Live Stream Pose-Estimation |
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- https://github.com/WongKinYiu/yolov7
- https://github.com/augmentedstartups/yolov7
- https://github.com/augmentedstartups
- https://learnopencv.com/yolov7-object-detection-paper-explanation-and-inference/
- https://github.com/ultralytics/yolov5 -https://github.com/RizwanMunawar?tab=repositories