What is a good video surveillance recording system ? Is it simply a system that provides storage capacity, reliability and redundancy ?
Certainly not. At least not only. Indeed these qualities are necessary but the ability of the recording system to help finding near real-time events or forensic events is key. Too much time is lost searching for specific excerpts and too few time is available when dramatic events happen.
That is where video analytics come in the loop.
Dealing with video analytics, one often refers to real-time alarm detection based on image analysis. A wide variety of algorithms have been proposed, some of them running on dedicated network appliances, reading a video stream and analysing it on the fly, some others directly running on the IP encoders and cameras boards.
Nevertheless, a different approach of video analytics is of high interest and has been proposed by some high end video surveillance vendors : the forensic analytics. While the algorithms remain the same, they are applied to a recorded stream instead of a live feed. Hence it is not about being alerted in real-time but about finding relevant video evidences using analytics as « filters » that help isolate images of interest.
While forensic analytics prove to be an efficient ally in chasing relevant images in a video archive, it should not be forgotten that they usually require a lot of processing power. That is why, just like in the real-time analytics use case they are two different system architectures. The on-board or embedded forensic analytics run directly on the NVR (Network Video Recorder) hosting the recorded video. This architecture is limited by the CPU power of the NVR server and that is one of the reasons why very few manufacturers propose NVR with embedded analytics and if they do, limit the number of concurrent filters. On the other hand, the dedicated video analytics server is hosted on a specific server, gets its video streams from the VMS (Video Management Server) and sends ananlysis results back to the VMS.
The latter architecture is way more scalable and maintainable but it has a major drawback, the NVR capacity to serve multiple concurrent stream requests from multiple analytic algorithms.
Industrial solutions like Agent VI, in cunjonction with VMS like Genetec Omnicast promise
« Apply an unlimited number of analytics rules of any kind and combination to each camera in parallel to the video recording. »
This is a very interesting value proposition, indeed.
Nevertheless, the bottleneck of such architecture remains the source of the video that will feed the analytics server. While the analytics servers cans be parallelized using as many of them as required, the video storage cannot be replicated. Hence, the solution to this resides in a storage system with massive concurrent access capacity, which opens a completely new field of investigation for the security systems architect. The industrial NVRs, inherently limited both by their bandwidth and by their processing power have to be replaced by a new storage system, able to centralize storage of huge numbers of streams and on the other hand to distribute a large number of video feeds to a large number of analytics servers.
This is the challenge that a company like Quantum has taken up with its StoNext system. StorNext is offering a filesystem interface with impressive scalability and performance, very well suited to video surveillance stringent requirements.
The StorNext Storage Area Network uses fiber channel block level technology and delivers a maximum throughput of 8Gbps that is instrumental to safe and efficient storage of thousands of cameras. It has been tested successfully during a certification process with Milestone VMS
Not only does StorNext simplify and streamline the management of the video storage, furthermore it allows moving transparently to a whole new paradigm of forensic and near real-time parallel video analytics that is urgently needed to absorb the gigantic video loads required by anti-terrorism and homeland security.
Food for thoughts.537a5237-1611-4195-a6d6-e893f61db6e3-170206120354