Video Analytics on the Edge Goes Mainstream

It was not long ago that government and businesses were resigned to paying telecom companies large annual sums for dedicated leased lines. Needless to say, the annual revenue cost of leasing these private lines has been extremely high, if not prohibitive.

Then along came broadband, and the opportunity to view live images or retrieve previously recorded video over the Internet became a possibility for almost any business, with the added benefit of being able to remotely view images on a PC and, more recently, even from a tablet or smartphone.

The broadband challenge
We have all got used to seeing the quality and sharpness of the images that can be delivered into our homes via high-definition TVs, so it is not surprising that users expect the same evidence-grade images from their video surveillance systems. This presents both a network bandwidth and a storage challenge, because high-def cameras require large amounts of both.

The arithmetic is simple: A 1080p stream at full-frame rate captured over 31 days by a single 2-megapixel camera will require about 1,000GB of network bandwidth and storage, and so, for a site with a large number of cameras, it is unlikely to be realistic or affordable to monitor or record all the cameras in real-time.

The solution for most video surveillance projects is to compromise either on frame rates, resolution, or both. It is not unusual for a system to be configured whereby high-resolution images are recorded in real-time and stored for just two or three days, with a separate video stream of low resolution or lower frame rate images stored for longer periods.

The video analytics option
Video analytics can provide a cost-effective method of reducing the amount of video that has to be transmitted when there is an alarm event.

When first introduced, video analytics was only available as a centrally based server solution. Recent improvements however in the design and processing power of digital signal processor (DSP) chipsets has made it possible for video analytics software to operate highly effectively at the edge. By this we mean the software is incorporated into individual cameras (or into encoders connected to existing analogue cameras), instead of having to be located on a central PC or server. This offers a major benefit to IP network-based video surveillance systems in that HD cameras can be deployed without excessive network bandwidth utilisation.

Users can take advantage of simple on-screen tools to define what events are important to them. Detection lines and zones can be configured to ensure that only specified activity is recorded.

Direction of movement can be defined so that alerts are only triggered when individuals or objects move in a particular direction across a zone, or when they move into a zone but not when they leave it. Filters provide the software with the ability to only detect adult people of a specific size and ignore false alarms from small animals and children.

The key to success is to specify video analytics software which has a proven track record of working reliably in a wide range of environments. A high detection rate and a low false alarm rate is essential in order for the software to have credibility.

The ability of video analytics to detect specific events means that users can save on both storage and bandwidth requirements. When a pre-defined event takes place, all of the relevant video can be stored on the camera’s SD card, whilst the event data and a small video clip can be instantly transmitted to a remote location. The viability of recording and storing video on the edge has been enhanced by the fact that 64GB SD cards are now readily available and 128GB and 256GB SDs are on the horizon.

The decision on where to transmit video to and where to store it will depend on the size and type of surveillance system and what it is intended to be used for, as well as the costs linked to bandwidth usage, installation, and the overall total cost of ownership, including the skill set of the people required to operate and maintain the systems.

If users decide on a remote “centralised” video storage solution, there is now the added option of cloud-based services offered by server warehouses. The charges for using these services are more than likely to be justified by not having to incur the cost of purchasing, housing, managing, and maintaining in-house servers. As more service providers offering cloud-based services enter the market, the resulting competition will inevitably push charges in a downward direction. However, without edge-based video analytics, a cloud-based service (or VSaaS) is unlikely to be suitable for video surveillance systems that have more than four or five cameras.

The business case for video analytics
The business advantage cameras equipped with video analytics can provide is compelling.

In addition to offering security personnel a very powerful tool to detect and combat theft and other criminal activity, video analytics is capable of multi-tasking, and so, depending on the field of view, a camera equipped with the technology should be able to simultaneously generate reports with supporting video to provide merchandising, human resources, security, and other operational departments with the information they need to achieve their objectives.

Every retail store will, for example, have its own set of challenges, but common to all is likely to be a desire to fully understand why a particular store is performing better than others and if specific marketing activities or promotions have been effective. Video analytics is able to make a significant and profitable contribution to finding out the answer to these crucial questions.

The good news is that more and more video management software (VMS) developers and network camera manufacturers are recognising that video analytics is the next big opportunity for installers and system integrators to win new business and, most importantly, deliver added value to users who are looking to maximise their return on investment in a video surveillance system.