AI Video Surveillance

 Video Surveillance systems are extensively deployed in various settings, including public areas, public infrastructures, commercial buildings, and more. In most cases, they are used for a dual purpose: real-time monitoring of physical assets and spaces and reviewing collected video information to identify security indicators and plan security measures. 

 Even though video surveillance systems have been an integral part of the public and security sectors for decades, there is a significant interest in them outside those industries. This interest is primarily due to increased crime rates and security threats all around the globe, which are driving the continued growth of the video surveillance market. According to a recent report by Mordor Intelligence, the video surveillance market was valued at $29.98 billion in 2016 and is expected to reach a value of $72.19 billion by 2022. This market potential is also propelled by recent advances in IT technologies—boosting video surveillance solutions' intelligence, scalability, and accuracy.  

 Recent advances in signal processing enable the development of intelligent video surveillance systems, notably systems that can flexibly adapt the rate of video data collection. In particular, whenever a security incident indicator is detected, the data collection rate is increased to provide richer information for more accurate and credible analysis. 

  2016 and 2017 were critical years for the history of Artificial Intelligence due to the emergence of disruptive deep learning approaches, like the ones employed by Google’s Alpha AI engine. The evolution of deep neural networks can be directly exploited in video surveillance systems to endow them with exceptional intelligence and enable more effective surveillance processes. For example, AI can help predictive analytics, allowing security operators to anticipate and proactively prepare for security incidents. 

  Beyond the specification of a proper edge computing architecture, video surveillance system deployers like Athena Tech also have to deal with other challenges. One of these challenges concerns the safeguarding of privacy and the adherence to data protection regulations. Indeed, the deployment of surveillance sensors is subject to laws and directives about privacy and data protection, which sometimes impose limitations on the nature and scale of the deployment. That is why Athena Tech designs its video surveillance solutions with a firewall at the edge providing maximum security.

That's why Athena Tech complies with standards and regulations, while also adopting a gradual/phased deployment approach. The latter should enable a smooth transition from manual i.e. human-operator-mediated systems to fully automated visual surveillance based on AI. A gradual deployment of data-driven intelligence is also required, starting from simple rules and moving to more sophisticated machine learning techniques that could detect more complex asymmetric attack patterns. Another best practice is the deployment of open architectures that can accommodate both future and legacy surveillance sensors, as a means of leveraging advanced functionalities at the best value for the money. Overall, modern video surveillance solutions can be very innovative, as they can comprise leading-edge IT and networking technologies. 

   Devising and implementing a proper architecture for your video surveillance infrastructure is essential. 

Modern video surveillance system architectures follow the edge/fog computing paradigm to process video information closer to the field. This allows them to economize on bandwidth and perform real-time security monitoring. Cameras are deployed at the network's edge as part of edge nodes that can capture and process video frames. Edge nodes can also implement data collection intelligence by tuning frame rates based on the identified security context. Moreover, they are connected to a cloud infrastructure, where information from multiple cameras is connected, reviewed, and analyzed at coarser time scales.