alert law enforcement if detected. By leveraging advanced technologies, these AI-enabled systems can even detect concealed weapons, enhancing safety and helping prevent violent incidents in both public and private spaces [8]. Most weapon detection methods rely on deep learning-based object detection using binary classifiers where the firearm is visually identifiable within the scene [9; 10; 11], with classifiers trained on both normal and abnormal samples. In light of this serious concern, several AI-powered commercial tools have emerged, recognized for their advancements in weapon and threat detection systems [12; 13; 14]. These tools leverage AI to enhance security by identifying potential threats in images and video frames, improving accuracy, and reducing response times. [PERSON] et al. [15] proposed a weapon detection system based on an ensemble of semantic convolutional neural networks (CNNs). Their approach decomposes the task of detecting and locating a weapon into a series of smaller subproblems, each focused on identifying individual component parts of the weapon, resulting in more accurate and robust detection. In another approach [16], transfer learning using VGG-16 as a feature extractor was employed to extract visible gun features, which were then classified by state-of-the-art support vector machine (SVM) classifiers [17] trained on a typical gun database. The model's performance was evaluated in various real-world scenarios, including different backgrounds with firearms, occlusion, and other challenges. Very recently, a two-stage gun detection (TSGD) method was proposed [18], in which Stage 1 utilizes an image-augmented model to effectively classify videos as \"gun\" or \"non-gun\". For videos classified as \"gun\", Stage 2 employs an object detection model to precisely locate the firearm within the video frames. Previous research has also explored various AI-driven approaches to developing reliable surveillance systems based on human action recognition in videos. However, most existing video analysis models (e.g., [PERSON] et al. [19]; [PERSON] et al. [20; 21]) are primarily designed for general video action recognition and have been evaluated on benchmark datasets. Ref. [19] introduced MoViNets, a model designed for efficient video recognition. Their approach incorporates stream buffers, which process videos in small consecutive subclips, maintaining constant memory usage while preserving long-range temporal dependencies. This innovation enables online inference while reducing memory and computational costs. One line of research in action recognition, ref. [20], explored transformer-based models for long-range spatiotemporal context modeling in video recognition. While self-attention mechanisms capture the global context effectively, they come with high computational costs. To address this, they proposed Video-FocalNets, a novel and efficient architecture that integrates both local and global contexts. Video-FocalNets utilizes a spatiotemporal focal modulation approach, reversing the interaction and aggregation steps of self-attention to improve efficiency. Another approach [21] focuses on spatiotemporal convolutions for video analysis, particularly in action recognition. The motivation behind this work stems from the strong performance of 2D-CNNs applied to individual video frames. Their study highlights the advantages of 3D-CNNs over 2D-CNNs within a residual learning framework. Additionally, they demonstrate that factorizing 3D convolutional filters into separate spatial and temporal components leads to significant accuracy improvements. Despite advancements in action recognition, these models often struggle with firearm detection due to the limited availability of labeled video datasets for training [18]. Through an empirical analysis of existing video action recognition models for robbery detection using a firearm video dataset that includes visible guns and dangerous poses, we evaluate the effectiveness of action recognition in firearm detection.