Categorization refers to the process of assigning predefined labels or classes to input data based on the patterns and features observed in the data. Categorization is a type of supervised learning where a model is trained on a labeled dataset, and its goal is to learn a mapping from input features to predefined categories. It is also commonly referred to as classification. Categorization is a fundamental task with wide-ranging applications, and its effectiveness depends on the quality of the training data, the choice of features, and the selection of an appropriate classification algorithm.

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

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.