Unmanned aerial vehicles for human detection and recognition using neural-network model

HIGHLIGHTS

• What: To enhance accuracy and performance, the authors propose a new system that concentrates on aerial RBG data and does not rely on depth information. The authors propose a new system that detects human action from aerial RGB videos, addressing the limitations of the previous work. The approach that the authors propose is designed to deal with these issues, particularly for RGB videos captured by drones. When tailoring the YOLOv9 algorithm for individual detection, the main goal is to accurately forecast bounding boxes with strong confidence scores, particularly for the human class.
• Who: Yawar Abbas . . .
  

   

  

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