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Intelligent Video Surveillance: Recent Trends And What Lies Ahead

Updated: May 11


Intelligent Video Surveillance: Recent Trends And What Lies Ahead

Closed-circuit television (CCTV) and other video surveillance systems are widely used in a number of contexts, including public spaces, public infrastructure, commercial properties, and so on. They are often employed for a dual purpose: real-time monitoring of physical assets and places, as well as reviewing video data to detect security indications and prepare security actions.


Despite the fact that video surveillance systems have been a staple of the public and security sectors for years, they continue to spark attention outside of those areas. This interest stems mainly from rising crime rates and security concerns across the world, which are pushing the video surveillance industry forward.


According to a recent study by Mordor Intelligence, the video surveillance market was valued at $29.98 billion in 2016 and is projected to reach $72.19 billion by 2022. Recent advancements in IT technologies have also boosted the intelligence, flexibility, and precision of video surveillance systems, increasing market potential. What drives the major video surveillance technology trends? How can you put them to the greatest possible use?


The following technological developments are driving the growth of video surveillance systems:


Intelligent and Context-Aware Video Data Collection

Recent advancements in signal processing have opened the way for the establishment of intelligent video surveillance systems, particularly those that can modify the pace at which video data is collected. When a security event indicator is identified, for example, the pace of data gathering is raised to offer richer data for more accurate and trustworthy analysis.


Big Data Infrastructures

State-of-the-art The large-scale data infrastructure has created new horizons for storing and retrieving 4Vs of large-scale data: volume, speed, versatility, and veracity. For example, it is now simpler than in the past to gather huge volumes of information from many cameras, particularly high throughput streaming data. Big data systems offer the tools for the smooth and economic development and implementation of video surveillance architectures.


Data Streaming Systems

Several streaming systems have developed in the past several years. While being an essential component of the mentioned earlier Big Data systems, the latter offers steam control and streaming analytics capabilities.


Predictive Analytics and Artificial Intelligence (AI)

The development of disruptive deep learning techniques, such as those used by Google's Alpha AI engine, made 2016 and 2017 significant years in the history of Artificial Intelligence. Deep neural networks' development may be directly utilized in video surveillance systems to provide them superior intelligence and allow more effective surveillance operations. For instance, AI may provide predictive analytics, allowing security operators to anticipate and prepare for security problems.





Integrating Physical and Cyber Security

The continuous digital transformation of commercial assets and processes is bringing physical and cybersecurity measures closer together. Because video surveillance systems are IT infrastructures that can be used to observe physical locations, they play an important part in this convergence. As a result, they may be easily linked with other cybersecurity systems to provide more holistic and integrated protection and surveillance strategy.


Architecting Video Surveillance Systems

The technologies mentioned above open up new possibilities in designing, implementing, and operating intelligent video surveillance systems. However, it is up to video surveillance developers and installers to integrate and properly use these systems' features. To that aim, a suitable architecture for your video surveillance system must be developed and implemented.


Modern video surveillance system designs use the edge/fog computing concept to analyze video information closer to the field. They may save bandwidth and conduct real-time security monitoring as a result of this. Cameras are used as part of edge nodes that can collect and process video frames at the network's edge. Edge nodes may also incorporate data gathering intelligence by tuning frame rates depending on the security context detected.


Furthermore, they are linked to a cloud infrastructure, which connects, reviews, and analyzes data from numerous cameras at coarser time scales. Edge/fog computing architectures are also excellent options for integrating video surveillance with the technologies discussed. As part of a mobile edge computing architecture, IoT drones must be integrated with suitable edge nodes. Real-time streaming analytics must be done at the edge of the video surveillance deployment instead of in the cloud. Deep learning capabilities may be used at the edge as well as in the cloud. Deep neural networks at the edge may assist in the real-time extraction of complicated security patterns.


At the same time, deploying deep learning in the cloud is the only way to extract security patterns and information about surrounding lands covered by multiple edge nodes (e.g., city-wide installations). In general, deciding whether certain functions should be stored in the cloud or at the edge is difficult. The resolution of trade-offs is typically linked with relevant choices (e.g., speed of processing vs. accuracy of processing for some surveillance function).


Open designs from various hardware manufacturers may help video surveillance systems. This is because various video capture devices and modalities (e.g., high-definition cameras, wired and wireless cameras, cameras on drones/UAVs, and more) may be used in a surveillance system. An open architecture may provide versatility, simplicity of deployment, and long-term technical viability. Over the last year, efforts have been made to establish an open, standards-based architecture for edge/fog computing, with video surveillance as one of the important aspects.




Concerns and Best Practices for Deployment

Beyond defining a suitable edge computing architecture, video surveillance system installers must contend with several additional problems. One of these issues is the confidentiality of personal information and respect to data protection laws. Surveillance sensor deployment is, in fact, governed by privacy and data protection laws and directives, which may place restrictions on the type and scope of the deployment. Similarly, the usage of drones should respect all legal requirements.


Further difficulty is the solution's level of automation. While automation is generally desired for reaching and monitoring larger regions without requiring more human resources, human review and intervention remain crucial to the entire solution's dependability. Another issue is the potential for new threats to emerge as a result of the cyber-physical aspect of video surveillance systems. A physical security event may be accompanied by a cyber-attack on video surveillance infrastructure, undermining the latter's capacity to detect the physical safety issue.


Another issue is the deployment of data-driven intelligence (e.g., as part of predictive analytics and AI), which needs huge amounts of data containing security events that are uncommon. Although the development of smart businesses with edge AI products and services, AI at the edge (e.g., lightweight and efficient deep neural networks) is still in its infancy.


To meet these difficulties, video surveillance solution developers and deployers must respect more rigorous standards and laws while also using a gradual/phased rollout strategy. The latter should allow for a seamless transition from manual, human-operated systems to completely automated AI-based visual monitoring. Data-driven intelligence must be gradually deployed, beginning with basic principles and progressing to more advanced machine learning methods that can identify more complicated asymmetric attack patterns. Another recommended practice is to use open architectures that can handle both future and old surveillance sensors to get the most out of advanced features for the right amount of money.


Modern video surveillance systems may be very innovative since they can include cutting-edge IT and networking technologies. What is the best method for upgrading or deploying your own video surveillance system? The sky is the limit once you have a comprehensive description of your security and business needs and a dependable technology partner that will integrate and implement the system on your behalf.