Our goal is to use cutting-edge deep learning security technologies to revolutionise public safety. The MERN stack, a potent blend of Express.js for dependable backend development, React.js for dynamic user interfaces, Node.js for effective server-side operations, and MongoDB for adaptable data storage, forms the basis of our solution. Scalability, real-time monitoring, and smooth system integration are all guaranteed by this stack.
Our anomaly detection skills are powered by deep learning technologies. Convolutional Neural Networks (CNNs) are highly effective in picture identification tasks, which allows us to reliably recognise physical altercations such as fights. Sequence prediction and analysis are the areas of expertise for Long Short-Term Memory (LSTM) networks, which are essential for identifying odd behaviours like unauthorised activity or strange walking patterns. TensorFlow offers the functionality that Python, the main language used to create these deep learning models,
Real-time processing and data labelling were two obstacles that needed to be overcome with great care and optimisation. Now that anomalies are being detected by our system with ease, prompt alerts are sent out for quick action and preventative security actions.
In the future, our deep learning security system will improve situational awareness in public areas, crowd control, emergency response times, and public safety all at once. It symbolises a paradigm change towards safer, smarter settings where technology constantly defends and strengthens communities