Author: Pavel Mishchenko, Specialist in IT infrastructure and integrated security of critical facilities
Abstract: The article examines the impact of artificial intelligence technologies, in particular neural networks and machine learning, on the paradigm of ensuring the safety of industrial and critical facilities. The aim of the work is to analyze the transition from passive video surveillance systems to proactive intelligent video analytics systems. The theoretical foundations of machine vision are considered, as well as practical scenarios for its application: from intrusion detection and control over the use of personal protective equipment to predictive analysis of abnormal behavior and monitoring of technological processes. The study found that the introduction of AI video analytics can significantly increase the efficiency of monitoring, reduce the workload of operators, and minimize the risks associated with the human factor. The practical significance of the work lies in the systematization of knowledge about the possibilities and challenges of implementing intelligent systems, which can serve as a basis for making strategic decisions when upgrading security systems at industrial enterprises.
Keywords: Artificial intelligence, video analytics, neural networks, machine learning, industrial safety, situational awareness, predictive analysis, critical infrastructure.
Traditional video surveillance systems at industrial facilities, despite their ubiquity, face a fundamental problem: exponential growth in the volume of visual data. One operator is physically unable to effectively monitor dozens and hundreds of video streams in real time, which inevitably leads to the omission of a significant part of the incidents. The relevance of the topic is due to the emergence and development of artificial intelligence (AI) technologies capable of analyzing video data at a level comparable to human, and even surpassing it in tasks requiring constant concentration. The security paradigm is shifting from after-the-fact analysis of archived records to automatic detection and prevention of threats in real time. The purpose of this article is a scientific analysis of the principles of operation and practical advantages of AI video analytics, as well as an assessment of its impact on the integrated security of critical facilities.
From fixation to understanding: the theoretical foundations of AI analytics
Classic video surveillance systems operate on the principle of a digital video recorder, only recording and storing data. The simplest motion detectors, responding to pixel changes in the frame, generated a large number of false alarms caused by weather events or animal movement. The revolutionary shift was made possible by the use of convolutional neural networks (CNNs), which are able not only to capture movement, but also to classify objects in a frame: a person, a car, an animal, and so on [2].
Unlike rigid logic algorithms, neural networks are trained on huge amounts of data, forming the ability to recognize complex images and patterns. This process, as described by its classics in the field of pattern recognition, allows the system to move from the analysis of low-level features to a high-level, semantic "understanding" of the scene [1]. The system is able to distinguish a person walking along a sidewalk from a person climbing over a fence, which is a qualitative leap in reducing false alarms and increasing the relevance of events that require operator attention.
Practical application scenarios at industrial facilities
The integration of AI into video surveillance systems opens up a wide range of application scenarios that go far beyond perimeter security. At industrial facilities, video analytics is becoming a comprehensive monitoring tool.
One of the directions is to monitor compliance with safety regulations. Neural networks are able to detect in real time whether employees have personal protective equipment (PPE): helmets, vests, glasses. If a violation is detected, the system can automatically generate an alert for the responsible person.
Another area is monitoring of technological processes. Video analytics can monitor the position of shut-off valves, detect steam or smoke leaks in the wrong places, and monitor the presence of foreign objects in dangerous areas of equipment operation. This helps to prevent not only safety incidents, but also industrial accidents.
In addition, behavioral analysis modules are widely used, which identify non-standard behavior: fights, crowding of people in an unauthorized area, abandoned objects or loitering on the territory [4].
Predictive analytics and anomaly detection
The next stage of development is the transition from the detection of pre-determined events to predictive analysis. Systems based on machine learning algorithms are capable of studying the "normal" state of an object for some time: typical routes of movement of people and vehicles, standard time of activity in various zones. After completing the training period, the system begins to record any deviations from the established norm.
For example, the appearance of a car in an area where there is usually no traffic at a given time of day will be regarded as an anomaly and will require the operator's attention. This approach allows you to identify previously unknown types of threats and emergencies. According to researchers in the field of intelligent systems, the ability to detect anomalies without prior knowledge of their nature is one of the most promising areas in ensuring preventive security [3]. This shifts the focus from responding to incidents that have already occurred to preventing them based on the analysis of indirect signs.
Challenges and limitations of implementation
Despite the significant advantages, the introduction of AI video analytics is fraught with a number of challenges. Significant computing power is required to process video streams in real time, which entails costs for server hardware or cloud services. In addition, the accuracy of neural networks strongly depends on the quality and representativeness of the training data, as well as on the conditions at the facility: lighting, weather conditions, camera angle. There are also ethical issues and risks associated with data privacy and the possibility of algorithm bias. Ensuring the cybersecurity of the video analytics system itself is becoming a critical task, since its compromise can lead to serious consequences.
The analysis shows that artificial intelligence and video analytics are fundamentally changing the approach to ensuring industrial safety. They transform video surveillance systems from passive recorders into active and intelligent proactive monitoring tools. The ability of neural networks to classify objects, analyze behavior, and detect anomalies makes it possible to automate the threat detection process, minimize the human factor, and increase overall situational awareness.
The practical application of AI analytics at critical facilities is already solving the tasks of perimeter control, safety compliance and monitoring of technological processes. The further development of these technologies towards predictive analysis opens up prospects for preventing incidents before they actually occur. Despite the existing technological and ethical challenges, the strategic advantages of implementing intelligent video analytics make it an integral part of the integrated security systems of the future.
List of literature
- Duda R. O., Hart P. E. and Stork D. G. Classification of patterns. - 2nd ed. - Wiley-Interscience, 2000. - 688 p.
- Lekun Yu., Bengio Yu., Hinton G. Deep learning // Nature. - 2015. - Volume 521, №7553. - pp. 436--444.
- Chandola V., Banerjee A., Kumar V. Anomaly detection: a review // ACM Computing Surveys. - 2009. - Volume 41, №3. - Article 15.
- Valera M., Velastin S. A. Intelligent distributed video surveillance systems: an overview // Proceedings of the IEE - Vision, image and signal processing. - 2005. - Volume 152, №2. - pp. 192--204.