Unlocking new potential in video security through AI

Rapid technological advancement, artificial intelligence (AI) and machine learning (ML) are revolutionising traditional on-premises video security systems. These next-level tools are not just enhancing video data capabilities; they're transforming how businesses approach security, operational efficiency, and information analysis.

Video analytics have been a part of security systems for many years, but the arrival of deep learning in 2009 marked a turning point. By training neural networks, basic analytics tasks like motion detection, object detection, and tracking objects within scenes have become commonplace. This leap forward has paved the way for more sophisticated AI and ML applications in video security.

Proactive security measures

AI-powered systems can perform complex tasks such as pose estimation and anomaly detection

Today's AI-powered systems can perform complex tasks such as pose estimation, anomaly detection, and behaviour analysis. These capabilities extend far beyond simple, passive monitoring, offering organisations rich insights and proactive security measures. 

For instance, analytics can now determine whether people are engaged in hostile or benign interactions, recognise unusual events that may signal safety hazards, and even predict potential security breaches before they occur — all based on analysing massive amounts of data that humans alone could never process.

Enhancing on-premises infrastructure

While the power of AI and ML in video security is clear, integrating these technologies into existing on-premises systems presents both opportunities and challenges. One of the primary considerations is the increased demand for processing power and storage capacity. As solution technology expands, hardware requirements will increase. This reality necessitates a strategic approach to system design and implementation.

Organisations must carefully evaluate their current infrastructure and plan for future needs to ensure their on-premises systems can handle the computational demands of AI and ML tools. However, the benefits often outweigh the challenges. AI-enhanced on-premises systems offer several advantages:

• Real-time processing: On-premises AI can analyse video feeds in real time, allowing for immediate response to security threats.
• Data privacy: Keeping data processing on-site can help organisations meet strict data privacy regulations and protect sensitive information.
• Customisation: On-premises systems allow for greater customisation of AI models to meet specific security needs.
• Reduced latency: Processing data locally eliminates the need for constant cloud communication, reducing latency in critical security applications.

The role of open platform video technology

To fully leverage AI and ML capabilities in on-premises video security systems, open-platform video management software (VMS) plays a crucial role. An open platform VMS allows for seamless integration of various AI and ML tools, cameras, and other security devices, creating a highly flexible and scalable system.

An open VMS can integrate thousands of cameras and sensors, allowing for centralised management and analysis of vast amounts of data. This approach enables security teams to quickly adapt to new threats and implement new and unplanned AI and ML solutions as they become available.

Video system management 

The hybrid approach to video system management combines on-premises infrastructure with cloud services

It's important to note that many organisations choose to deploy a hybrid approach to video system management that combines on-premises infrastructure with cloud services. This strategy can offer the best of both worlds: the control and low latency of on-premises systems with the scalability and advanced capabilities of cloud-based AI and ML tools.

For example, some cities have implemented hybrid data storage models, hosting critical real-time data on local servers while leveraging cloud services for long-term storage and advanced analytics. This approach allows for efficient management of large amounts of high-resolution video data while reducing costs associated with on-premises storage expansion.

Practical applications and benefits

The integration of AI and ML into on-premises video security systems is transforming security practices across industries, offering benefits that extend beyond traditional surveillance. These advanced technologies enhance security measures while providing valuable insights for operational efficiency and strategic decision-making.

By analysing video data in real time, AI and ML-powered systems can detect patterns and automate responses in unprecedented ways. Here are some key examples of sector-specific benefits:

• Retail: AI-powered analytics can optimise product placement, track shopping patterns, and enhance loss prevention efforts.
• Education: K-12 schools can use advanced video analysis to address issues like vaping and bullying, monitor traffic, ensure that proper procedures are followed, and provide enhanced safety and security.
• Manufacturing: AI can streamline quality control processes, detect safety violations, and optimise production line efficiency.
• Healthcare: Intelligent video systems can monitor patient safety, manage access control, and even assist in documenting and verifying that procedures and protocols are followed appropriately.
• Transportation: AI-enhanced video systems can improve traffic management, enhance security in transit hubs, and assist in incident response.

Challenges and considerations

Ensuring access to robust, diverse, and representative data sets is essential for training AI models effectively

AI and ML hold great promise for on-premises video security, but organisations may encounter challenges during implementation. The considerable upfront costs could discourage smaller businesses or those with tight budgets. Nevertheless, this should be viewed as a long-term investment with significant returns in enhanced security and operational efficiency.

Implementing AI-powered systems in video security can be complex, often requiring specialised skills, potentially creating a gap within existing IT or security teams. To bridge this skills gap, organisations may need to invest in training or partner with external experts to address this challenge. Additionally, the quality of data is crucial for effective AI and ML implementation; poor or insufficient data can result in inaccurate analyses and unreliable results. Ensuring access to robust, diverse, and representative data sets is essential for training AI models effectively.

Benefits of integrating AI and ML

Ethical considerations surrounding privacy, consent, and potential algorithmic bias are also critical. Organisations must strike a balance between enhancing security and safeguarding individual privacy rights to maintain public trust in these technologies.

Despite these challenges, the benefits of integrating AI and ML into on-premises video security systems often outweigh the difficulties. Careful planning, resource investment, and a strong focus on ethical and regulatory compliance can lead to more effective, efficient, and intelligent security solutions.

Future outlook

Edge computing capabilities will enable sophisticated AI processing directly on cameras

The future of AI and ML in on-premises video security promises significant advancements that will address current limitations and unlock new possibilities. 

Edge computing capabilities will enable sophisticated AI processing directly on cameras and other security devices, reducing strain on central servers and potentially lowering hardware requirements. This, combined with more efficient AI algorithms, will democratise access to advanced AI and ML capabilities for organisations of all sizes.

AI-powered analytics

Predictive analytics will become a cornerstone of future video security systems, marking a shift from reactive to proactive security measures. As AI models become more sophisticated, their ability to anticipate and prevent security incidents will improve dramatically, revolutionising risk management and incident response. 

The integration between video security and other business systems will deepen, with AI-powered analytics providing insights beyond security into business operations and strategic decision-making.

Data for training AI models

Explainable AI will become more overall, which is vital for building trust in automated systems

Automation of security processes will reach new heights, freeing human operators to focus on high-level decision-making and complex situations. To support this evolution, we'll likely see increased use of synthetic data for training AI models, addressing privacy concerns, and improving model robustness. Explainable AI will become more prevalent, which is crucial for building trust in automated systems and meeting regulatory requirements.

For security professionals, embracing these technologies is no longer optional but necessary to remain competitive and provide the best possible service to clients. By leveraging open platform VMS and carefully planning system architectures, organisations can create flexible, scalable, and powerful video security solutions that not only protect assets but also drive business value.

Enhanced video security systems

The key to success will be finding the right balance between on-premises control and cloud-based capabilities while addressing important considerations around privacy, ethics, and regulatory compliance.

With thoughtful implementation and ongoing adaptation, AI and ML-enhanced video security systems will continue to play an increasingly central role in safeguarding our businesses, institutions, and communities.