## 2 Related Work and Our Contributions  
AI-powered imaging technology helps detect active shooter threats before a firearm becomes visible or shots are fired. Ref. [22] introduced a radar sensor-based 3D imaging approach for walkthrough screening systems, designed to enhance security in crowded areas by detecting concealed weapons on moving individuals. Their method leverages a fully convolutional architecture to generate a 3D peak-shaped probability map for effective weapon screening. For an efficient surveillance system, integrating advanced imaging technology with deep learning is essential for detecting firearm threats and ensuring public safety in both public and private spaces [23]. Furthermore, once a firearm becomes visible, the visual features extracted from camera can be combined with action recognition techniques to enhance the performance of AI-enabled surveillance systems. In [24], a CNNs model was utilized to detect and classify normal and abnormal activities in video frames extracted from imaging device. Human action recognition techniques focusing on posture and human body landmarks have been applied to analyze various physical conditions [25], including _handgun_ detection [26], sports assistance [27], fall detection [28], and children's gross motor skill analysis [29]. Recently, a new strategy has emerged that combines multiple neural networks (NN) with the DEtection TRansformer (DETR) network for firearm detection in images [30]. The methodology presented in their work promotes collaboration and self-coordination among networks in the fully connected layers of DETR using multiple self-coordinating artificial neural networks (MANNs), eliminating the need for a coordinator. This self-coordination involves training the networks sequentially and integrating their outputs without additional components, resulting in high-level outcomes with a firearm detection precision of 84%. Extracting information specific to human actions is an effective method for detecting abnormal poses including firearm detection, offering robustness against complex backgrounds, illumination changes, and dynamic camera scenes [31]. In [7], the integration of skeleton features with visual features presented a promising yet underexplored approach for firearm detection, highlighting the need for further research in this area. They employed CNN- and transformer-based architectures for visual feature extraction and integrated these visual features with language models for firearm detection using a simple CNN model [7]. [PERSON] et al. [32] proposed a method for analyzing silhouettes to recognize the classic holdup position in armed robberies. Their approach involves segmenting the silhouette's skeleton into distinct body parts and identifying arm positions. Their results demonstrate that leveraging skeleton-based analysis effectively identifies human body parts and accurately detects holdup positions. Detecting active hands holding a visible firearm adds complexity to active shooter identification [33]. In some scenarios, individuals such as security personnel or police officers may already be present in the frame while holding firearms in normal poses, making it difficult to extract distinctive features for identifying active threats. Human pose estimation (HPE), which predicts the positions of body parts and joints in images or videos, enables the analysis of activities involving major muscle groups in the torso, arms, and legs. In recent years, various HPE methods [34; 35] have played a critical role in enabling sign language recognition, full-body gesture control, and the quantification of physical exercises. These advancements serve as a foundation for applications in sports, fall detection, children's gross motor skill assessment, and other fitness-related areas. Rather than scanning the entire frame, skeleton landmarks can be leveraged to pinpoint hand positions, and human activity recognition can be integrated with visual features to enhance firearm detection system performance.