Video Surveillance Bandwidth Requirements pertain to the amount of data that the video surveillance system will transmit over a network. High-resolution video feeds and the number of cameras are among the factors that significantly impact bandwidth needs. Efficient video compression algorithms can help in reducing the bandwidth requirements. Understanding these needs is critical when planning a video surveillance system, as insufficient bandwidth can lead to choppy video, delays, or even system failure.

With cutting-edge AI technology, including real-time object detection and facial recognition, you can transform your computer into a powerful security system. Computer vision technology of today is powered by deep learning algorithms that use a special kind of neural networks, called convolutional neural network (CNN), to make sense of images. These neural networks are trained using thousands of sample images which helps the algorithm understand and break down everything thats contained in an image. These neural networks scan images pixel by pixel, to identify patterns and memorize them. It also memorizes the ideal output that it should provide for each input image (in case of supervised learning) or classifies components of images by scanning characteristics such as contours and colors. This memory is then used by the systems as the reference while scanning more images. And with every iteration, the AI system becomes better at providing the right output.