With the expansion of urban space and the increase in population in big cities, the use and expansion of video systems in urban space, subways, stations, gathering centers, etc. has become an important issue.
In the past decades, finding operators who are willing to review videos for information or to find a specific point, or at least familiar with this task, has become a big challenge for all managers.
Due to the large amount of data, this issue is not unreasonable at all, because these systems are collecting information far more than what is needed, information that plays an important role in the safety and security of our urban life, but on the other hand High volume and storage are also a problem.
This is the point where artificial intelligence or deep learning enters the field. Of course, it cannot be said that the large amount of data is an inhibiting factor because this data is considered an auxiliary tool.
Deep learning can continuously collect and monitor information and help adapt your system to new environments
According to the sales manager of Geovision Taiwan Company: it takes about 4 to 6 months for our research and development team to prepare an artificial intelligence algorithm for a new location and customize it, which, of course, will include many bugs. has it.
Therefore, today’s deep learning algorithms are intelligently improving computer vision and video analysis. Powerful systems are trained according to the environment in which they are placed and personalized for different conditions without the need to rewrite codes.
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Systems equipped with deep learning against old algorithms!
With the help of deep learning, computer vision techniques such as face recognition and motion recognition have become much more sophisticated than before, and this has transformed surveillance and other video functions.
According to the tests performed in a controlled environment, older algorithms performed well, but these types of algorithms are usually designed and coded for some specific cases and in a specific location.
For example, detecting an object or a person crossing a pre-defined virtual line is considered a simple yes or no algorithm. It becomes challenging to use these algorithms when they are used in far more complex scenarios. When we use an old algorithm in the location of different cameras, some of these cameras may be in different spaces such as highways, and parks. or the lobby of a hotel, these environments are seen in the recorded videos in different ways that the old algorithms cannot handle subtleties.
For example, in a busy street where different people are constantly moving, motion detection systems may give unauthorized and irrelevant alarms.
With the help of deep learning, a person’s face can be registered in the database with just a photo or video, so after that, the software will automatically search all the surveillance videos recorded during the past months and find the person. It is not possible to work using old algorithms.
Deep learning algorithms can be trained according to the following different conditions to analyze and investigate complex issues:
- Counting people or objects moving in two different directions
- Identification and recognition of people’s faces for different applications
- Masking people’s faces when they are recognized in the video, for privacy
- Defogging videos recorded in foggy conditions to view images clearly
- Connect and paste recorded videos from different cameras into a single panoramic view
- Video balancing in a shaky environment
- Counting people in places with occupancy code restrictions
- Eliminating waves and vibrations caused by wide-angle lenses
- Smart search for an event in a busy area
How do deep learning systems work?
These systems include; There are cameras, recording servers, and a video control center. These cameras are connected through a standard and secure protocol and are processed by processors such as Intel.
By using powerful processors, not only does the performance and power of video analysis increase 8-10 times, but also their storage space is optimized.
The advantage of cameras equipped with a deep learning system is that instead of sending all the videos to the central operator, they send warnings as soon as they detect the illegal case and red lines and reduce the time delay before any action is taken. they give.
Most cities today have video systems that have multiple cameras, ports, and software at the same time. Interfaces and software development kits generally enable communication between hardware and software and are managed by a single cloud management software.
systems based on deep learning; Smart and scalable
By using systems based on deep learning and the ability to integrate them with other hardware and software, big cities can use different solutions to grow the level of video surveillance. In this regard, deep learning improves the level of operational efficiency by improving automatic reactions and combining and matching them with the necessary standards.
In addition, you can combine different systems on this basis, for example, combine anti-fire and anti-theft systems (facial recognition systems for access control) and increase your work efficiency.