YOLOV8n Person Keypoint Detection Sample

  • Description
    Person keypoint detection is a task in computer vision and video analytics that involves identifying and localizing specific points of interest on the human body within images or video frames. These points, or "keypoints," typically correspond to joints and significant body parts such as the elbows, knees, shoulders, hips, and head. The goal is to detect these points in a way that allows for the analysis of human poses, movements, and activities.

    Keypoints are predefined spots on the human body that can be used to represent the posture or gesture of a person. Common keypoints include the major joints (ankles, knees, hips, spine, neck, elbows, wrists), as well as the tips of the head, nose, and sometimes fingers and toes.

    The keypoints are used in:
    • Motion Analysis: In sports and medical fields, analyzing how a person moves can provide insights into performance or physical conditions.
    • Activity Recognition: By understanding the pose of a person, algorithms can determine what activity they are performing, which is helpful in surveillance, human-computer interaction, and entertainment.
    • Augmented Reality: Keypoint detection can help in overlaying digital content onto people in real time, enhancing AR experiences.
    • Animation: In the film and gaming industries, keypoint detection can be used for motion capture to animate characters based on actual human movements.
    Person keypoint detection is a dynamic area of research and development in computer vision, with ongoing improvements in algorithms, training techniques, and computational efficiency. It is a foundational technology for many systems that require an understanding of human form and activity.

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  • Key Features
    — YOLOV8N-Pose KeyPoint Detection Model
  • End-To-End Performance
    — 178 FPS on Nvidia A4000
    — 56 FPS on Jetson NX
    — 67 FPS on Jetson Orin Nano