Real-Time Human Pose Detection With YOLOv8 Models
YOLOv8 is used in this project to identify human poses in real time. As the COCO dataset is used to train the model, its performance is checked, and poses in photos and videos are predicted. Pose recognition compresses the video output so that it can be s
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Project Outcomes
The real-time Human Pose Detection using the YOLOv8 project successfully achieved several key outcomes.
- The real-time Human Pose Detection using the YOLOv8 project successfully achieved several key outcomes.
- A complete real-time human pose detection system accurately tracking and identifying body poses from images and videos.
- Use an advanced YOLOv8 model for accurate and efficient detection.
- Achieves reliable pose estimation by identifying key body points and movements.
- Providing real-time pose detection in dynamic environments.
- Implements video compression with FFmpeg to optimize storage and improve output sharing.
- Applicable in security, healthcare, sports, and entertainment, offering versatile solutions to estimate pose detection and human activity tracking.