The advancements in video surveillance has improved our security in a significant manner. One of the major issues face in video surveillance is analyzing the huge amounts of video that has been recorded so as to find useful information. With the increase in the number of surveillance devices, the data generated is going to increase several folds.
Manually going through hours of video is a very tough task. The persons can get distracted and could lose focus during the moment that something occurs that requires their attention.
This is where artificial intelligence comes in.
Imagine a program that goes through this video to find data that could useful.
Let us take a crime scenario.
Imagine that the lookout is for a black SUV. The program can be provided with the data input of the video surveillance and also given instructions to only look out for black SUV vehicles. The immediate filtering of the huge amount of data can occur, all thanks to the power of data analytics. This could save hours of manual work.
The advancements in AI makes it possible for the business and law departments to take much more better decisions based upon the information obtained from the surveillance data and also improves the real time operating of these institutions.
One of the major advancements in the video surveillance field is the facial recognition technology. Facial recognition is made possible by the use of Deep learning and AI. Facial recognition allows for much faster working of the law organisations. Law enforcement agencies can make use of this technology to reduce the time taken to take the criminals in custody. This is also useful to the business owners to identify shoplifters.
The edge processing model and cloud computing has been a major factor in the improvements of the video analytics sector. The edge processing models reduces the time taken to get the result from the data by processing the data at the edge of the network itself. This reduces the transportation overhead that is associated with sending the data to any location. The cloud computing model allows users to have access to almost infinite computing power without buying any additional hardware. With the improvements in the processing powers of personal computers, there is going to be increase in the edge computing models and this will in turn improve the video surveillance.
Deep learning algorithms can be taught to learn from the data that they process. This means that as they continue processing more data, they get smarter and also are able to provide better insights in the future.
In the future video content analytics will expand into several fields including transportation, higher education, healthcare, retail and much more. This is going to be used in the future not only to improve the security but also to improve the efficiencies of the working of organisations. This could include identifying steps to be taken to improve the working of the organisation and also to improve the revenue generation.
These were the ways how intelligence is and will be improving the video surveillance in the future.