Over the past few decades, video surveillance systems have evolved significantly from traditional analogue systems that simply record footage to more sophisticated, intelligent systems capable of real-time analysis and decision-making. Initially, surveillance 5G cameras acted as passive tools, capturing footage that would later be reviewed by human operators or stored for future reference. While these systems served their purpose of monitoring premises and recording incidents, they lacked the ability to actively analyze and respond to events as they unfolded. Today, with the advent of AI Video Analytics in Modern Surveillance Systems, surveillance technology has transformed into a proactive security solution, enabling automated threat detection, behavioral analysis, and instant alerts.
However, the advent of AI video analytics marks a transformative shift in surveillance capabilities. Powered by cutting-edge artificial intelligence (AI) technologies, such as machine learning (ML), computer vision, and neural networks, modern video surveillance systems can now analyze video streams in real-time, enabling rapid identification of critical events and proactive security measures. This shift is driven by the need for more efficient, scalable, and intelligent solutions that go beyond traditional methods of security.
AI video analytics works by processing video data to detect specific objects, behaviours, or patterns, allowing for automatic responses based on predefined parameters. The technology continuously learns and improves its ability to recognize and interpret complex scenarios, such as identifying suspicious behaviour, recognizing faces or license plates, or detecting anomalies in movement. This makes AI-powered surveillance systems far more accurate, faster, and reliable compared to conventional manual monitoring techniques.
In a world where security threats are becoming more sophisticated and widespread, AI-driven video analytics offer organizations the ability to quickly identify potential risks before they escalate, enhancing both safety and operational efficiency. Whether it’s identifying unauthorized access to restricted areas, tracking movements in large crowds, or monitoring equipment for safety compliance, AI video analytics provides real-time insights that human operators may otherwise miss.
This technological leap is particularly important as the scope of surveillance expands, not just in private enterprises but across smart cities, transportation networks, healthcare facilities, and critical infrastructure. The capability of AI to process vast amounts of video data at scale provides a level of situational awareness that is unmatched by older surveillance systems, paving the way for smarter, more effective security operations.
Ultimately, AI video analytics represents a key component of the future of security, allowing organizations to move from reactive security measures to proactive threat mitigation, enhancing both public and private safety while driving operational efficiency across various sectors..
What is AI video analytics in modern surveillance systems?
AI video analytics refers to the use of sophisticated artificial intelligence (AI) algorithms to automatically process and analyze video footage captured by surveillance cameras; unlike traditional systems, which simply record and store video, AI video analytics systems are designed to interpret the content of the video in real-time or during post-event reviews. The core objective is to extract meaningful insights from video data that can assist in decision-making, enhance security, and improve operational efficiency, all with minimal human intervention.
AI video analytics leverages a range of advanced technologies, most notably:
- Computer Vision: This is the foundation of AI video analytics. Computer vision allows machines to “see” and interpret visual information in a way that mimics human vision. It enables systems to identify objects, people, and scenes within video frames, just as a human would. For example, computer vision can recognize a person’s face, track the movement of a vehicle, or detect an object left unattended.
- Object Recognition: AI video analytics uses object recognition algorithms to identify and classify various objects or entities within a video stream. These could range from specific items like bags or vehicles to broader classifications like people or animals. Object recognition is particularly useful in applications such as intrusion detection, vehicle tracking, or asset protection, where the system is trained to detect particular objects of interest.
- Facial Recognition: One of the more advanced features of AI video analytics is the ability to identify or verify individuals through facial recognition technology. By comparing facial features against a database of known individuals, AI systems can quickly verify identities, monitor access to secure areas, and track the movements of individuals within a given environment. This is particularly valuable for high-security facilities or places where identity verification is crucial.
- Behaviour Analysis: AI video analytics goes beyond merely detecting objects or people — it can also assess behaviours and activities. Using behavioural recognition algorithms, the system can identify unusual or suspicious actions such as loitering, running, or aggressive behaviour, enabling immediate alerts or interventions. This is especially useful for public safety, crowd management, and anomaly detection in large-scale environments like shopping malls, transportation hubs, or city streets.
- Motion Detection and Tracking: Traditional motion detection relies on detecting movement within a specific area, but it often produces false positives due to environmental factors like lighting changes or animals. AI video analytics enhances motion detection by tracking the direction, speed, and pattern of movement, helping to distinguish between typical movements (e.g., pedestrians walking) and suspicious behaviour (e.g., someone entering a restricted area). This enables more accurate threat detection with fewer false alarms.
Real-time vs. Post-event analysis
AI video analytics supports both real-time and post-event analysis, which makes it a versatile tool for a wide range of applications:
- Real-Time Analysis: In real-time, AI video analytics continuously processes video feeds, analyzing events as they happen. This allows for immediate detection of suspicious activities, such as unauthorized access, theft, or vandalism. AI systems can then trigger automated alerts, notifying security personnel or even taking automated actions like locking doors or triggering alarms. Real-time analysis is crucial for proactive security measures, allowing organizations to intervene before an incident escalates.
- Post-Event Analysis: While real-time analysis focuses on immediate threats, post-event analysis allows security teams to go back and review video footage after an incident occurs. AI-powered tools enable rapid searches of historical footage based on specific criteria, such as objects, people, or behaviours. This helps reduce the time it takes to identify key events during an investigation. Additionally, AI systems can analyze large volumes of archived footage and automatically tag or categorize events, making it easier for investigators to find the information they need.
Proactive incident management and investigation
What sets AI video analytics apart from traditional surveillance is its ability to provide actionable intelligence in a proactive manner. Instead of simply recording footage for later review, AI systems actively interpret the data, allowing organizations to respond to events more effectively. For example, in a crowded area, AI can identify when a person behaves unusually or enters a restricted zone, triggering an immediate alert for security staff to investigate or respond.
In the case of investigations, AI video analytics can help reconstruct events with greater speed and precision. Instead of manually sifting through hours of footage, security teams can use AI to search for specific individuals, vehicles, or objects of interest, filtering out irrelevant data. The ability to tag events with metadata — such as timestamps, object recognition labels, or behaviour descriptions — allows for more accurate and comprehensive investigations.
The growing importance of AI video analytics
As the volume of video data generated by surveillance systems increases, the need for AI-powered analytics becomes even more critical. Traditional methods of manually reviewing footage are simply no longer feasible in large-scale environments. AI video analytics helps bridge this gap by providing automated, scalable solutions that ensure no critical event goes unnoticed.
In industries ranging from retail to transportation to healthcare, AI video analytics is becoming an essential tool for enhancing security, improving operational workflows, and driving actionable insights. By leveraging the power of AI, organizations can make more informed decisions, mitigate risks, and foster safer environments for employees, customers, and the general public.
Key benefits of AI video analytics
AI video analytics offers a wide array of advantages that are transforming surveillance operations across industries. The technology’s ability to automate video monitoring, analyze large amounts of data in real-time, and provide actionable insights allows organizations to enhance their security measures, streamline operations, and reduce costs. Here are some of the key benefits:
Enhanced threat detection and faster response
AI video analytics significantly improves the ability to detect suspicious activities in real-time. Unlike traditional surveillance systems that rely solely on human monitoring or simple motion sensors, AI systems can identify specific behaviours like loitering, unauthorized perimeter breaches, or the presence of unattended bags or objects. This proactive monitoring allows security teams to receive immediate alerts when these events are detected, enabling them to respond faster and more effectively. In high-risk environments such as airports, stadiums, or government buildings, this rapid detection can be crucial in preventing or minimizing potential security threats.
Reduction in false alarms
Traditional motion detection systems are prone to generating false alarms due to environmental factors such as moving trees, changes in lighting, animals, or even weather conditions. These false positives can overwhelm security teams, leading to alert fatigue and reduced response efficacy. AI video analytics improves accuracy by leveraging sophisticated algorithms that can differentiate between benign movements and genuine security threats. By analyzing patterns of motion and behaviour over time, AI can more accurately determine what constitutes a real threat, minimizing unnecessary alarms and ensuring that security personnel only respond to critical incidents.
Increased operational efficiency
AI video analytics helps organizations optimize their security operations by reducing the need for constant human intervention. With automation handling routine monitoring tasks such as identifying common events (e.g., door openings, employee movement), security personnel can focus their attention on more critical incidents that require human judgment or decision-making. The system’s ability to monitor large volumes of footage simultaneously, without fatigue or distraction, leads to more efficient use of security resources and faster identification of potential issues.
Scalability and adaptability
One of the significant advantages of AI video analytics is its scalability. Whether it’s a single building, a multi-site campus, or an entire smart city infrastructure, AI-powered surveillance systems can be easily expanded to meet growing security needs. AI systems can adapt to various use cases, from monitoring parking lots and retail stores to tracking public spaces in metropolitan areas. As security requirements evolve, these systems can seamlessly integrate with new sensors, cameras, and technologies, ensuring that they remain effective in the face of changing environments and emerging threats.
Improved forensic investigations
AI video analytics significantly accelerates post-event investigations by providing powerful tools for searching and analyzing footage. Security personnel can quickly search for specific events, objects, or individuals, filtering vast amounts of recorded video data in seconds. For example, in the aftermath of a security breach or an incident, investigators can search for specific behaviours (e.g., suspicious behaviour, unauthorized access) or track individuals or vehicles across different camera feeds. This capability drastically reduces the time spent reviewing hours of video footage manually, enabling faster identification of suspects and clearer evidence for legal or compliance purposes.
Long-term cost savings
While the initial investment in AI video analytics can be considerable due to the cost of hardware, software, and integration, the long-term savings make it a sound investment. By automating routine surveillance tasks, organizations can reduce the need for large security teams or eliminate the costs associated with continuous human monitoring. Additionally, AI’s ability to reduce false alarms decreases the frequency of unnecessary security dispatches, resulting in fewer resource allocations and operational disruptions. Over time, these savings in manpower reduced downtime, and more efficient incident response can lead to substantial reductions in operating costs.
Return on investment and business impact
The implementation of AI video analytics can yield measurable benefits in terms of both financial return on investment (ROI) and business impact. AI systems help mitigate risks by providing enhanced surveillance and early detection of potential threats, which can ultimately protect valuable assets, ensure the safety of employees and customers, and safeguard critical infrastructure. Furthermore, by improving operational efficiency, companies can redirect resources toward more strategic initiatives. AI video analytics can also help organizations maintain compliance with regulatory standards (e.g., GDPR, HIPAA), reducing the risk of penalties and enhancing organizational reputation. By contributing to improved safety, cost reductions, and better resource management, AI-powered surveillance solutions offer significant business value across various sectors, from retail to healthcare to transportation.
Integration with security and enterprise systems
AI video analytics does not operate in isolation; rather, it can seamlessly integrate with existing surveillance infrastructure and other enterprise systems to create a unified, cohesive security solution. This integration enhances the functionality of both the video analytics platform and the broader security ecosystem, ensuring a more comprehensive approach to threat detection and response. Below are key areas where AI video analytics can be integrated into existing systems:
Access control systems
AI video analytics can work in tandem with access control systems to verify identities and detect unauthorized entries. For example, the system can use facial recognition to compare the faces captured on camera with a database of authorized personnel, granting access only to those with valid credentials. If an unauthorized individual is detected attempting to enter a restricted area, AI can trigger an immediate alarm or alert security personnel. This integration helps create a more robust and efficient identity verification process, reducing human error and enhancing security at entry points.
Alarm systems
AI video analytics can also enhance traditional alarm systems by correlating visual data with intrusion or fire alarms. For example, if an alarm is triggered by motion or a breach in the perimeter, AI systems can automatically analyze video footage to verify the cause of the alarm. By identifying whether the breach is a false alarm or a legitimate security threat (such as an intruder or fire), AI systems can reduce false positives and provide security teams with the necessary information to take appropriate action. This integration minimizes response time and helps avoid unnecessary dispatches.
IoT devices
The Internet of Things (IoT) plays an increasingly important role in modern security and building automation. AI video analytics can integrate with IoT devices such as smart lighting, HVAC systems, and security sensors to enable intelligent, automated responses based on occupancy and environmental changes. For example, if AI detects movement in a building during off-hours, it can trigger the lighting system to turn on, adjust temperature settings, or lock doors for security. In larger facilities or smart cities, this integration supports energy efficiency, automated safety protocols, and a more responsive security system that adapts to real-time conditions.
Centralized command centers
For larger organizations, centralized command centres serve as the nerve centre for monitoring and responding to security incidents across multiple sites. AI video analytics can integrate with these command centres to provide a unified view of the surveillance feeds and real-time data from various systems, allowing for more coordinated and efficient responses. By combining video analytics with other security measures (such as alarms, access control, and IoT devices), AI can present a comprehensive picture of any ongoing incidents, helping security personnel assess the situation more quickly and make informed decisions. This integration also facilitates faster incident coordination across platforms, ensuring that appropriate teams are mobilized and that response efforts are aligned across the organization.
Enhanced situational awareness and proactive security
The interoperability of AI video analytics with other enterprise systems significantly improves overall situational awareness. By providing a holistic view of security data, organizations can detect and respond to threats more proactively. Instead of relying on isolated systems that operate independently, AI analytics enable seamless data flow and communication between systems, allowing for more intelligent decision-making and faster reactions to emerging incidents.
This integrated approach helps organizations not only enhance their security posture but also streamline operations, reduce operational silos, and foster a more coordinated and efficient security environment. Ultimately, AI video analytics enables smarter security by providing actionable intelligence in real-time, empowering organizations to stay ahead of potential threats.
Industry use cases
Retail
Detects shoplifting, analyzes customer behaviour, and optimizes store layouts through heatmaps.
Transportation
Supports crowd management, vehicle tracking, and intrusion detection in high-traffic zones.
Smart Cities
Enhances public safety with automated monitoring of streets, intersections, and public areas.
Healthcare
Monitors patient safety, detects unauthorized access to restricted areas, and supports compliance with health regulations.
Critical Infrastructure
Secures power plants, data centres, and water treatment facilities through perimeter protection and intelligent threat detection.
Industrial and Manufacturing
Ensures workplace safety, monitors for hazardous conditions, and enforces protocol adherence.
Deployment considerations
Deploying AI video analytics successfully requires careful planning and consideration of several key factors. From infrastructure readiness to regulatory compliance, ensuring that all components are aligned is critical for maximizing the effectiveness of the system. Below are the main considerations for a successful deployment:
Infrastructure readiness
For AI video analytics to function optimally, the underlying infrastructure must be robust. This includes ensuring that high-resolution IP cameras are in place to capture detailed video data, as the accuracy of AI analysis depends heavily on the quality of the footage. Additionally, a stable network bandwidth is essential to ensure that video streams can be transmitted without interruptions, especially in real-time applications. Adequate storage solutions are also required to handle the vast amounts of video data generated by surveillance systems, particularly when the system supports high-resolution video feeds or is configured for long-term data retention. Whether using on-premises storage or cloud-based storage, scalability and redundancy are crucial to ensure seamless access and security of stored footage.
Deployment models
Organizations can select from various deployment models based on their specific latency, control, and compliance needs. These models include:
- Cloud-Based: Cloud deployment offers scalability and flexibility, with the ability to store and process data remotely. It is ideal for organizations that need to access footage and analytics from multiple locations or require less upfront capital investment in hardware.
- On-Premises: On-premises deployment allows for full control over the system, data, and security, making it suitable for organizations that require high levels of control, security, and compliance. This model often involves significant infrastructure investment but may be preferred in highly regulated environments.
- Hybrid (Edge): Hybrid or edge-based deployment combines both cloud and on-premises models, allowing data processing to occur locally at the edge of the network (near the cameras) while still enabling centralized storage and analysis. This model reduces latency, enables real-time processing, and supports the efficient handling of large video data volumes.
Choosing the right deployment model depends on the organization’s specific requirements, including the desired level of control, speed of response, and data storage capacity.
Privacy and regulatory compliance
AI video analytics systems must comply with relevant privacy and regulatory requirements, such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), or other regional data protection laws. These regulations ensure that the video data collected is handled appropriately, protecting individuals’ privacy rights. To comply with these laws, AI video analytics solutions must-have features like:
- Data Masking: Sensitive information, such as faces or personal identifiers, should be obscured or anonymized to maintain privacy.
- Encrypted Storage: Video data must be securely stored and transmitted using encryption to prevent unauthorized access or tampering.
- Access Control: Only authorized personnel should have access to the video data and analytics, ensuring that the system is used responsibly and legally.
By adhering to these regulations, organizations can ensure that their AI video analytics solutions do not violate privacy rights or expose them to legal risks.
Scalability and flexibility
AI video analytics systems should be scalable and adaptable to meet changing security needs over time. As an organization grows, so too should its surveillance and analytics capabilities. A system that is easily scalable allows for the addition of new cameras, sensors, or analytics features without requiring a complete overhaul of the infrastructure. The system should also be flexible and capable of evolving to accommodate new use cases, such as integrating with emerging technologies or expanding to new locations.
Moreover, flexibility in deployment models (e.g., hybrid or cloud-based solutions) allows organizations to adjust based on shifting business needs, operational challenges, or budget constraints, ensuring that the surveillance system remains effective and aligned with the organization’s strategic goals.
Challenges and iimitations
While AI video analytics offers significant benefits, there are several challenges and limitations that organizations must consider during deployment and operation. Understanding these hurdles can help organizations make more informed decisions about implementing AI-driven video surveillance systems. Below are some of the key challenges:
High initial investment
One of the most significant challenges of AI video analytics is the high initial investment required for deployment. These systems typically involve substantial upfront costs, including expenses for high-resolution cameras, specialized hardware (such as GPUs for processing), software licenses, and the integration of the analytics platform into existing infrastructure. Additionally, if the system is cloud-based, there may be recurring costs for data storage and processing. While these costs may be justified by long-term savings and improvements in security, the initial financial commitment can be a barrier for some organizations, particularly small or budget-constrained ones.
Continuous model training
AI video analytics systems rely on machine learning algorithms that are trained to recognize patterns, objects, and behaviours. However, these models are not static; they require ongoing updates and retraining to adapt to different environments, evolving threats, and new types of data. For instance, an AI system deployed in a new environment (such as a different geographical location or industry) may need adjustments to its training data to improve accuracy. Similarly, as security threats evolve, models must be updated to recognize new patterns or behaviours. This continuous need for model training can incur additional operational costs and may require skilled personnel or external expertise to manage the process effectively.
Privacy concerns
The use of AI video analytics, especially technologies like facial recognition, raises significant privacy concerns. Organizations must ensure that they use these technologies in a responsible and ethical manner, particularly when collecting and analyzing sensitive data. Strict policies must be in place to manage how data is collected, processed, stored, and shared to avoid infringing on individuals’ privacy rights. In many jurisdictions, the use of facial recognition and other biometric technologies is subject to legal restrictions, and organizations must adhere to regulations such as GDPR or CCPA (California Consumer Privacy Act). Additionally, there may be public concern about surveillance overreach, which could impact an organization’s reputation if not managed transparently and ethically.
Proper data retention policies are also crucial to ensure that video data is not stored for longer than necessary, reducing the risk of misuse or potential breaches.
Dependence on video quality
The effectiveness of AI video analytics systems is heavily dependent on the quality of the video footage. Poor video quality, such as low resolution, low light conditions, or improperly positioned cameras, can reduce the accuracy of AI algorithms and lead to missed threats or false positives. For example, if cameras are not placed in optimal locations or lack adequate coverage of key areas, the system may fail to detect suspicious activities or identify individuals correctly. Similarly, poor lighting or environmental factors like fog, rain, or glare can impact the system’s ability to interpret visual data accurately. Ensuring that the cameras are of high quality, positioned correctly, and maintained properly is crucial for achieving the full potential of AI video analytics.
Frequently asked questions (FAQs)
How accurate is AI video analytics in real-world surveillance environments?
Accuracy can exceed 90% in real-world environments when AI video analytics is configured properly. This involves using high-quality video feeds, ensuring optimal camera placement, and fine-tuning the system for specific environmental conditions. However, the system's performance may vary depending on the complexity of the environment, such as lighting conditions, camera resolution, and movement patterns.
Can AI video analytics replace human surveillance operators?
AI video analytics significantly enhances the efficiency of human operators by automating routine monitoring tasks, such as identifying motion or unusual behaviour. However, it does not completely replace human oversight. Human judgment is still required for complex decision-making, such as evaluating ambiguous situations or responding to nuanced security threats. AI works alongside human operators, enabling them to focus on critical tasks that require a higher level of discretion and decision-making.
Is AI video analytics suitable for small and medium-sized businesses?
Yes, AI video analytics is increasingly accessible to small and medium-sized businesses (SMBs) due to cloud-based platforms and scalable solutions. These businesses can adopt AI-driven surveillance without the need for heavy capital investment in on-premises infrastructure. Cloud solutions allow for flexible pricing models and the ability to scale as needed, making it a cost-effective option for SMBs looking to enhance their security.
What type of camera infrastructure is required for AI video analytics?
High-definition IP cameras are ideal for AI video analytics because they provide clear, detailed video feeds that are essential for accurate analysis. However, many AI systems are designed to be backwards-compatible with existing camera setups, meaning organizations can often integrate their current surveillance infrastructure into an AI analytics solution. Upgrading to higher-resolution cameras is recommended for optimal performance, but the system can still function with older cameras as long as they meet minimum quality standards.
How is video data stored and secured in AI surveillance systems?
Video data can be stored either on-premises or in the cloud. Regardless of the storage option, data is typically secured using encryption, access control mechanisms, and secure transmission protocols. On-premises storage provides more direct control over security, while cloud storage offers scalability and remote access. Both options should implement strong data protection measures to ensure the privacy and integrity of stored video data, with strict guidelines on data retention and access management.
Can AI detect complex or unusual behaviour patterns?
Yes, AI video analytics can identify complex or unusual behaviour patterns. Advanced AI models are trained to recognize various forms of anomalous behaviour, such as aggressive movements, suspicious loitering, or irregular activity that deviates from normal patterns. These systems can automatically flag such behaviours for further review, enabling a more proactive response to potential threats or incidents that would be difficult for human operators to detect in real time.
What are the limitations of AI video analytics?
Despite its advanced capabilities, AI video analytics has limitations, such as: • Reliance on high-quality input: Poor camera placement, lighting, or video resolution can reduce the system's effectiveness. • Potential algorithmic bias: Like all AI systems, video analytics may be affected by biases in training data, which could impact the accuracy of predictions or detections. • Ongoing maintenance: AI systems require regular updates, model retraining, and environmental tuning to stay effective over time. Addressing these limitations requires continuous monitoring, data quality management, and regular system updates.
How does AI video analytics contribute to compliance and regulatory requirements?
AI video analytics contributes to compliance with regulatory frameworks like GDPR, HIPAA, or CCPA by providing features that help with automated incident logging, access tracking, and audit reporting. These capabilities facilitate compliance with data retention and access control policies, ensuring that organizations meet legal and regulatory requirements regarding video surveillance and data privacy.
Can AI video analytics be integrated with existing security systems?
Yes, most AI video analytics platforms offer integration APIs or connectors that enable seamless interoperability with other security systems, such as access control systems, alarm systems, or IoT devices. This makes it easier for organizations to enhance their existing security infrastructure with AI-driven analytics without needing to completely replace their current systems. Integration helps create a unified security ecosystem for more efficient monitoring and response.
What are the long-term cost implications of adopting AI video analytics?
While the initial investment in AI video analytics can be high, organizations can see significant long-term savings. Reduced staffing needs due to automation, fewer false alarms, and enhanced incident response contribute to operational savings. In addition, the improved security and risk mitigation provided by AI systems can help protect valuable assets, reduce liabilities, and improve ROI. As the technology continues to evolve and become more accessible, the cost-effectiveness of AI video analytics will likely improve, making it a viable option for businesses of all sizes.
Final thoughts
AI video analytics is not just an advancement in surveillance technology; it represents a fundamental shift in how organizations approach security. By harnessing the power of artificial intelligence, organizations can move beyond traditional, reactive surveillance to embrace a proactive, data-driven approach that significantly improves response times, enhances threat detection, and ensures the safety of assets and individuals.
The true value of AI video analytics lies in its ability to automate routine monitoring tasks, reduce human error, and provide actionable insights that would otherwise be difficult or time-consuming to uncover. Whether it’s detecting suspicious behaviours, identifying potential risks, or improving operational efficiency, AI enables a more responsive, intelligent security system that can evolve with changing threats and environments.
Moreover, AI video analytics helps organizations stay compliant with increasingly stringent regulations by offering tools for automated reporting, audit trails, and data security. These capabilities not only protect organizations from potential legal liabilities but also ensure that privacy and data protection are maintained.
As AI technologies continue to mature, the role of AI video analytics in security systems will become not just beneficial but essential. Its ability to seamlessly integrate with existing infrastructure, scale across multiple locations, and adapt to new challenges makes it an invaluable tool for both large enterprises and small businesses. The long-term cost savings, operational efficiencies, and enhanced security it offers will drive continued adoption across a wide range of industries, ensuring that AI video analytics remains at the heart of modern security strategies.
Learn more about AI Video Analytics in Modern Surveillance here.
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