Threat Recognition Through Victim And Assailant's Pose And Used Threat Object By Applying YOLOv5s Algorithm

This study aimed to recognize threats by recognizing the assailant pose, victim pose, and the threat object used by the assailant in one frame in a threat emergency situation using a 2D camera and by applying
$YOLOv5s$
algorithm. The system's ability to correctly identify threats depends heavily on the training and labeling in
$YOLOv5s$
. Thus, the bounding boxes were carefully assigned, and the labels were arranged properly. Through the application of
$YOLOv5s$
algorithm, supervised learning was implemented. Recognized threats were identified by recognizing the three variables including, victim pose, assailant pose, and threat object in one frame. The
$YOLOv5s$
were able to localize the pose and object and avoid misclassification by setting the appropriate Intersection over Union (IoU) and confidence threshold. Using a truth table,
$YOLOv5s$
was able to identify threats by removing possibilities that were not even threats. As for the result, the system was able to recognize each of the assailant poses, victim poses, and threat objects in one frame. Thus, the system was able to obtain an overall reliability of 98.125%.