12/08/2024
Convolutional neural networks in Surveillance Sector:
In layman terms, learning the patterns or categories directly from the available data and taking decisions based on that data can be defined as Convolutional Neural Networks (CNN). Within the layers of the network, CNN uses filters to understand and recognise the patterns available in any data or image. Diving deeper, the filters are able to decode complex structures and read more patterns and match with the corresponding data. These inputs are then taken up for decision making, based on scenarios. The network thus does a deep learning of the existing data and then makes categories and classifications, based on that.
In classical surveillance industry scope, facial recognition and object classification are common known scenarios of CNN. Even though it has been in usage for few years now, deep Learning algorithms are not directly related to the CNN, so often. A face being spotted on a camera regularly can be automatically recognised and if manually approved, could be segmented for access requirements. As simple as that. That makes the implementation of the entire access management system for any existing restricted areas, as easy as it gets. Preconceived notions of different categories, can be regularly updated for the deep learning processes.
As the applications pan out, deep learning has become critical tool in medical diagnosis and scanning, object detections in industrial as well as commercial applications, etc.
If interested to dive deeper, read through an interesting piece of work, where CNN has been used in DDoS attack detection. The modes of preventing IoT being used for DDoS attacks, is also quite intriguing.
