Body Pose refers to the specific configuration or orientation of a person’s body as they walk through a screening system. This includes the positioning and alignment of different body parts, such as the head, shoulders, arms, and legs. The analysis of body pose is often part of security and screening systems designed to detect anomalies, threats, or suspicious behavior in individuals passing through the screening area.

In walk-through screening scenarios, especially in security checkpoints at airports, train stations, or other public places, automated systems may use advanced technologies such as computer vision, machine learning, or sensor networks to analyze the body pose of individuals. The goal is to identify deviations from typical or expected body postures that may indicate concealed objects, irregular behavior, or potential security threats.


Real-time ConcealedWeapon Detection on 3D Radar Images forWalk-through Screening System

This paper presents a framework for real-time concealed weapon detection (CWD) on 3D radar images for walk-through screening systems. The walk-through screening system aims to ensure security in crowded areas by performing CWD on walking persons, hence it requires an accurate and real-time detection approach. To ensure accuracy, a weapon needs to be detected irrespective of its 3D orientation, thus we use the 3D radar images as detection input. For achieving real-time, we reformulate classic U-Net based segmentation networks to perform 3D detection tasks. Our 3D segmentation network predicts peak-shaped probability map, instead of voxel-wise masks, to enable position inference by elementary peak detection operation on the predicted map. In the peak-shaped probability map, the peak marks the weapon’s position. So, weapon detection task translates to peak detection on the probability map. A Gaussian function is used to model weapons in the probability map. We experimentally validate our approach on realistic 3D radar images obtained from a walk-through weapon screening system prototype. Extensive ablation studies verify the effectiveness of our proposed approach over existing conventional approaches. The experimental results demonstrate that our proposed approach can perform accurate and real-time CWD, thus making it suitable for practical applications of walk-through screening.