Enhanced Gender Detection in Surveillance Systems Using YOLOv8 and Back Body Pose Analysis

An important facet of gender detection in surveillance systems is improving security, crowd monitoring, and behavioral analysis. This work explains advanced techniques for gender detection applications using the latest version of the You Only Look Once object detection algorithm called YOLOv8. YOLOv8, known for its real-time processing capacity and increased accuracy, is an efficient tool for surveillance applications. This article discusses YOLOv8 implementation in gender identification in challenging environments. Challenges from lighting conditions, occlusions, and angles of the cameras continue being exposed. Experiments show that YOLOv8 is robust enough to detect and classify gender accordingly using real-time surveillance footage. We also utilized the back body pose for analysis, whereas others employed front face or body poses. The work here provides insights for optimizing model parameters and enhances detection precision. This offers a very valuable tool for modern surveillance systems.