Legacy security systems, such as closed-circuit television (CCTV) surveillance or access control solutions, can significantly enhance protocols with modern technology. Machine learning (ML) is a powerful branch of artificial intelligence (AI) involving robust data analysis to identify trends and make accurate predictions. Integrating ML with existing video surveillance systems can be highly advantageous and ultimately supercharge security efforts.
Here, BCD Video has suggested 4 strategies for Integrating machine learning into legacy video infrastructure and presents the benefits and reasons for carrying out the transformation.
Seamlessly integrate machine learning into your legacy security video infrastructure with several strategies.
1. Edge processing
Edge processing involves leveraging machine learning data processing on edge devices close to where cameras capture video instead of centralised servers. When implementing edge processing, security teams must optimize the ML models with regular updates and establish hardware requirements to ensure edge device efficiency. This strategy helps keep sensitive data local without network delays.
2. Tiered storage
Another approach for integrating ML into an existing video security system includes creating a tiered storage architecture. This solution can optimise storage costs and make scaling storage capacity more flexible. Data can be organised into three main tiers:
● Hot: This tier offers high-performance storage and is used for active processing.
● Warm: This tier provides more cost-effective storage for your most recent security data.
● Cold: The cold tier stores historical security data.
3. Distributed computing
Machine learning can also integrate with a security video infrastructure with distributed computing. This involves leveraging distributed systems, enabling them to process video data across multiple devices, also called nodes. This type of computing enables parallel processing for real-time data analysis.
4. API middleware
An application programming interface (API) middleware is a software layer that can create a bridge between your existing security video systems and machine learning capabilities. A huge advantage of API middleware is that it does not require upgrading legacy systems. You can enjoy simplified integration while improving authentication, security and data format conversion.
Integrating machine learning for CCTV surveillance, access control systems and other legacy video security solutions can be highly beneficial. BCD Video presents some of the advantages of ML in video security below:
● Real-time threat detection – Machine learning solutions can enhance real-time threat detection, helping security teams stay vigilant and intervene promptly during potentially high-risk situations. ML algorithms can help flag suspicious behaviour, such as abnormal movement patterns or unauthorised access. Teams can identify risks and act faster to ensure optimised security.
● Advanced recognition – Integrating machine learning solutions into an existing security infrastructure can supercharge sophisticated recognition of everything from faces to products to license plates. Multi-camera tracking allows teams to track behaviour across different areas and support robust oversite. Advanced recognition capabilities enable security teams to have historical data that can be used to hold individuals accountable, whether dealing with theft or trespassing.
● Operational improvements – Machine learning tools can help security teams improve monitoring processes in several ways, including automating routine surveillance tasks. Security personnel can spend less time and energy on manual video footage review, enabling them to spend more time on high-value tasks to supercharge site safety. ML tools also enable continuous, real-time monitoring without requiring additional labour.
● Pattern recognition – A major advantage of introducing machine learning to a legacy surveillance systems is the ability to recognise patterns and trends. These tools can establish baseline behavioral patterns and detect abnormalities that could indicate a potential security risk to enhance security efforts.
● Data-driven insights – One of the greatest benefits of leveraging ML is generating actionable, data-driven insights from surveillance footage. Video analytics using AI and machine learning can optimise security operations by enabling evidence-based decision-making. You can use predictive analytics to assess potential security threats based on historical data.
● Contextual understanding – Advanced machine learning tools provide security teams with contextual analysis to provide greater insight into security events, helping to distinguish between normal behaviour and suspicious activity. Contextual understanding helps increase threat assessment accuracy while reducing false positives, thanks to environmental and historical data analysis
