Surveillance Performance Analysis of Vision Tasks in Common Device Applications
Keywords:
Artificial Intelligence, Renewable Energy, Cybersecurity, Sustainable AgricultureAbstract
This study delves into the effectiveness of keyword spotting and handgun detection tasks, widely employed for optimizing device control and surveillance systems. While deep learning approaches dominate these tasks, their performance is predominantly assessed in datasets of exceptional quality. This research aims to scrutinize the efficacy of these tools when applied to information captured by commonplace devices, such as commercial surveillance systems with standard resolution cameras or smartphone microphones. To achieve this, we propose the creation of an audio dataset comprising speech commands recorded from mobile devices and various users. The audio analysis involves an evaluation and comparison of state-of-the-art keyword spotting techniques against our own model, which surpasses baseline and reference approaches, yielding an impressive 83% accuracy. For handgun detection, we fine-tune YOLOv5 to tailor the model for accurate handgun detection in both images and videos. The model is rigorously tested on a novel dataset featuring labeled images from commercial security cameras. This comprehensive evaluation ensures a robust assessment of the model's adaptability and performance in real-world scenarios, providing valuable insights for the development and deployment of surveillance applications on common devices.
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