Weapon Recognition in CCTV Videos: Deep Learning Solutions for Rapid Threat Identification

The study offers a new dataset and method for real-time weapon identification in surveillance video using deep learning, which can identify weapons in surveillance video cameras or smart IP cameras and give real-time notifications to security staff. The paper discusses the challenges of detecting weapons in surveillance videos, including the variability of the pose and appearance of the hand weapons and the complexity of the background scenes. The proposed model uses a sliding window process and feature selection to accurately identify weapons in surveillance videos. The framework relies on the YOLOv8 algorithm and the PELSF-DCNN classifier, augmented for feature selection via the CSBO method. The suggested system demonstrates exceptional accuracy and minimal false positive rates by utilizing advanced deep-learning techniques and motion estimates. The authors test their methodology on a new dataset of video surveillance photos including hand weapons and show that it beats state-of-the-art algorithms in terms of detection accuracy and speed. They compare R-CNN and R-FCN real-time object detection algorithms with feature extractors VGG and ResNet. They also look at the implementation of transfer learning and data augmentation methods to increase model accuracy. The paper proposes a novel blending pose method to augment the training data and improve the robustness of the detection model to pose variations. The authors discuss the dataset folder structure, XML annotation format, and the edge/cloud framework used to deploy the system in the real world. They provide deep insight into the research findings and discuss the limitations and potential drawbacks of using this type of technology for surveillance. To identify potential instances of violence and deter criminal activities, it is recommended that the proposed system be integrated with existing surveillance cameras or intelligent IP cameras. The authors conclude their discourse with prospective future endeavors in this domain, including the utilization of increasingly advanced deep learning algorithms and the integration of supplementary varieties of sensors and data sources to enhance the precision and effectiveness of the system. Overall, this research provides insights into the potential of deep learning for improving public safety and security. The proposed system can be applied to real-time surveillance videos to detect potential violent situations and prevent crimes. The study emphasizes the need to establish effective solutions to reduce weapon violence and offers a potential way to accomplish this aim.