Video surveillance systems have come a long way from simple cameras that record what is happening on film to modern intelligent systems that utilize powerful artificial intelligence (AI) algorithms. Today, video surveillance not only records events, but also analyzes them in real time to help prevent crime and ensure security.

1. Evolution of video surveillance

The field of video surveillance systems started with simple analog cameras that recorded video on magnetic tape. These cameras were used for passive surveillance, most often in banking institutions and large stores, to monitor what was happening and provide video evidence in case of incidents.

The first video surveillance systems appeared in the mid-twentieth century. One of the most famous examples of the early use of video surveillance was the Bersucht system in Germany in the thirties, installed to monitor rocket launches. The progenitor of video surveillance systems is considered to be a German electrical engineer, Walter Bruch (second photo).

CCTV systems have undergone significant changes in appearance and technology since the 1940s. Here's what they looked like in different periods:


First CCTV systems:

- Low-resolution analog cameras were used.

- The cameras were large, bulky and required a lot of space.

- Video was recorded on magnetic tapes, which were then viewed on special monitors.

1950s and 1960s

Development of analog technology:

- Cameras became more compact, but were still quite large compared to today's standards.

- Videocassettes and reel-to-reel tape recorders were used to record video.

- Video surveillance began to be used in banks, stores, and public facilities for security.


The transition to more advanced analog systems:

- Image quality improved, and higher resolution black and white cameras became available.

- Cameras became more reliable and compact, allowing them to be installed in more locations.

- Videotape recorders (VCRs) became the standard for recording video, making it easier to store and play back recordings.


The digital age and the beginning of the transition to IP technology:

- Digital video recorders (DVRs) began to be used, which allowed video to be recorded on hard disks.

- Cameras became color cameras, which improved the identification of objects and people.

- The first remote access systems via modems and early computer networks appeared.

- Data compression technologies such as JPEG and MPEG began to be used to reduce the amount of video stored.

1990s: The advent of IP cameras

IP cameras and networked systems:

In the 1990s, IP cameras began to appear that could stream video over the Internet or a local area network. This made it possible to monitor objects in real time from anywhere in the world.

Advantages of IP cameras:

- High resolution and image quality.

- Ability to remotely access and control cameras via web browsers or mobile applications.

- Easy integration with other security systems.

2000s: Increased functionality and mass distribution

Intelligent features:

Cameras began to be equipped with features such as motion detection, video analysis, and facial recognition. This increased the efficiency of video surveillance systems and allowed many processes to be automated.

Mass distribution:

Video surveillance systems became more affordable, leading to their widespread use not only in commercial and government institutions but also in private homes.

2010s: Integration of artificial intelligence

Artificial Intelligence (AI):

AI began to be used extensively in surveillance systems for behavioral analysis, facial and object recognition, and predicting potential threats. This has improved the accuracy and speed of incident response.

Cloud technologies:

Cloud storage has simplified video management and made it possible to access data from anywhere, anytime.

Today: Modern video surveillance systems

Deep learning and neural networks:

Modern video surveillance systems use deep learning and neural networks to analyze video data more accurately. This allows for better recognition of faces, cars, and other objects.

Internet of Things (IoT):

Cameras have become part of the IoT ecosystem, allowing them to be integrated with other devices such as smart locks, sensors and home automation systems.


With the increasing use of network cameras, cybersecurity has gained a lot of attention to protect data and prevent hacking.

2. How does AI work in video surveillance?

Face Recognition:

AI is capable of identifying and recognizing faces in real time. This is used to control access, find suspects in a database, and improve security at facilities. Machine learning algorithms are trained on large datasets with facial images, allowing them to match and identify people efficiently

Behavior analysis and object recognition:

AI can analyze the behavior of people and objects, detecting suspicious or anomalous activities. For example, the system can alert security guards if someone stays in one place for a long time or moves through a restricted area.

AI can monitor people's movements by picking up anomalies - such as a person falling and then notifying a communications center.

AI algorithms can recognize and classify different objects such as cars, bags or weapons. This is used to automatically detect potential threats and improve security.

Motion detection:

AI helps detect motion in the frame more accurately by ignoring unnecessary objects such as small animals or light changes. This reduces false positives and increases system reliability.

Technologies and methods

Deep learning and neural networks:

At the core of AI in video surveillance are deep neural networks that are trained on large amounts of data. This allows systems to "learn" to recognize and analyze different scenarios and objects with high accuracy.

Real-time video analysis:

Modern AI systems are capable of processing video streams in real time, allowing for instant response to incidents. Powerful processors and specialized chips such as GPUs (graphics processing units) are used for this purpose.

Integration with other systems:

AI integrates with other security systems such as access control and alarm systems, creating comprehensive solutions for managing facility security.

3. Examples of AI applications in video surveillance

Smart cities:

As part of the smart cities concept, AI is being used to monitor public spaces, manage traffic, and provide security at public events.

Corporate Security:

In corporate offices, AI helps control access to buildings, track the movement of employees and visitors, and prevent theft and other incidents.

Home security:

AI in home surveillance systems allows users to receive notifications of suspicious events, view video through mobile apps, and secure their homes.

4. Benefits

Benefits of using AI in video surveillance:

- Increased accuracy: Eliminating human error and improving recognition accuracy.

- Resource savings: Reducing the workload of staff who used to monitor cameras.

- Rapid response: Instant response to potential threats.

5. The future of video surveillance with AI

Artificial intelligence (AI) video surveillance systems will continue to evolve, becoming more integrated and smarter. Here are some key areas where I believe they will evolve in the future:

Improved face and object recognition algorithms

AI systems will continue to get better at recognizing faces, objects, and behaviors. Deep learning algorithms and neural networks will become more accurate and efficient, enabling faster and more accurate identification of people and objects, even in challenging conditions such as poor lighting or viewing angles.

Integration with other smart systems

CCTV systems will be tightly integrated with other smart home and city technologies such as smart locks, lighting, sensors and access control systems. This will enable end-to-end solutions for security and automation of various processes.

Behavior analytics and predictive security

AI will be used to analyze behavior and predict potential threats. For example, the system will be able to detect suspicious behavior based on historical data and alert security in advance. This is especially important for preventing crime and accidents.

Improved data compression and transmission techniques

With the development of data compression technologies and increased network bandwidth, such as 5G, video surveillance will become even more efficient. This will enable the transmission of high-quality live video with minimal latency and storage costs.

Ethical considerations and privacy protection

The development of AI in video surveillance will be accompanied by addressing ethical and privacy issues. It is important to strike a balance between security and citizens' rights by implementing transparent and fair data usage policies.


As the number of connected devices and data volumes grow, cybersecurity will become a key priority. New methods will be developed to protect data, prevent hacking and secure surveillance systems.

Cloud technology and big data analytics

The use of cloud technology and big data analytics will become standard. Cloud platforms will utilize powerful analytical tools to process video data, providing valuable insights and improving the efficiency of systems.

Mobile and portable solutions

Video surveillance will become more mobile and portable. Cameras embedded in drones and wearable devices will enable coverage of hard-to-reach or dangerous areas. This will open up new opportunities for the use of video surveillance in a variety of applications, from industry to public safety.

Automation and self-managed systems

The future also lies in the automation of video surveillance processes. Systems will be able to make decisions on their own based on data analysis, such as activating alarms or sending notifications to guards.

In general, video surveillance systems with AI will become more and more powerful and feature-rich, which will significantly increase the level of security and automation in various areas.