How Deep Learning Technologies Work and Its Benefits In Everyday Life

Among the information technology industry, the Deep Learning aspect that is part of Artificial Intelligence (AI) is now getting special attention. In an effort to realize the benefits and better classification of some technology applications.

Inspired by the workings of the human brain, this technology uses layered learning to help computers to classify, store and access data, which can refer to learning.

This means Deep Learning technology can use the whole image to recognize, rather than relying on a separate aspect of an image. This process is cumulative in that the more elements are used, the better the classification can be. That way, the aspect of ‘learning’ is increasing.

The use of Deep Learning for face recognition and image classification makes it even the most important technology in the security industry. The technology reaches every aspect of the security industry. Starting from the face and vehicle detection, to analyze behavior (behavior analysis). Thus, this technology began to change the focus of security activities from previously reactive, now able to predict the problems before they arise.

An example of using Deep Learning for facial recognition (Photo: NEC)

One company that is quite concerned in the development of technology-based Deep Learning is Hikvision. Manufacturers from China have been using the technology that is applied to a series of products. Like a camera with DeepInView IP and DeepInMind NVR capabilities simultaneously bringing the full power and benefits of Deep Learning technology.

On the one hand, the camera presents a sophisticated ‘eye’ for the system. NVR produces measurement capabilities (analytic) and brain storage. Various products help the security aspects in two ways – recognition, monitoring, and counting of people and the introduction and detection of vehicles. The use of Deep Learning technology is very effective regarding its ability to classify and recognize thousands of ‘features’.

Clearly, this multi-layered approach uses a lot of memory and performance. This is also one of the reasons why this technology has been widely spread over the past few years. To be clearer, in the early stages of the development of the technology, there are 1,000 devices with 16,000 CPUs needed to simulate neural networks. Now, only a few GPUs are needed.

Be it Intel, Nvidia, Hikvision is exploring various Deep Learning opportunities in the surveillance industry. Another innovation is to facilitate and improve the industry by utilizing H.265 + codecs that reduce the bandwidth transmission and data storage requirements. So, in theory, there is no decrease in quality even though the data is distributed and stored many times greater.

Application of this technology is many. For example, the technology that supports this system provides useful black list / white list alarms that are used in access control usage scenarios. The technology can also be used to recognize suspicious behavior. Enabling security staff to prevent the problems of people roaming around the site.

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