The cure to information overload is more information.
To a collector of curios, the dust is metadata. - David Weinberger
It's challenging and potentially error-prone to manually monitor video footage to identify events, incidents, and behaviours by sight. Some might even argue that it's an impossible activity. Can you relate?
To automate understanding of the enormous amount of data surveillance captures, we rely on machine-learning engines to build deep-learning algorithms. The primary objective of incorporating deep learning into VSaaS is to automatically get notified of temporal and/or spatial events and, in more advanced solutions, predict those recurring incidents and behaviours before they occur.
Deep learning is a subfield of machine learning that involves using neural networks to learn patterns and features from data to make decisions or predictions. The use of deep learning in surveillance monitoring significantly enhances the efficiency and accuracy of the surveillance process, equipping us to respond more quickly to potential threats or incidents.