Why are video analytics used in CCTV / video surveillance?
Intelligent video analytics offer many benefits to the CCTV / video surveillance industry. The main advantage that analytics software offers is the ability to automatically identify possible threats in real-time, taking a huge amount of pressure off of security staff. Other benefits include:
1. Increased speed and efficiency
CCTV monitoring operators have a challenging job: over shifts of several hours, they have to concentrate on screens and stay alert to any changes they spot. Even the slightest change could be an indication of some suspicious activity - and it is widely reported that control room operators review a minimum of 3 alarms every single minute.
However, when you consider that 95% of these alarms are likely to be false, operators end up wasting a lot of time and effort, which makes them less likely to perform at their best when a real incident occurs. Intelligent video analytics relieve the burden of false alarms by cutting up to 90% of them out of an operator’s workload, leaving them free to focus on the genuine ones.
By integrating technology, operators are much better equipped to react quicker and more efficiently to security threats. In the long term, this also boosts employee morale as the “noise” of false alarms isn’t the time-sapping factor it once was. Check out theCentre for the Protection of National Infrastructure’s Control Rooms Guidancefor more helpful information about false alarm management.
2. Highly accurate decision making
When developing algorithms for crime detection, accuracy is paramount - it could mean the difference between a crime being stopped, or a crime taking place.
Humans can detect incidents very accurately, under optimal conditions: assuming they’re not tired and their environment allows them to focus. However, most operators have to multitask, overseeing several monitors at one workstation. In the 1990s, Jim Aldridge conducted research for the UK Police Scientific and Development Branch (PSDB) which showed that as more monitors are added to an operator’s workstation, detection rates go down.
Aldridge looked at how well operators could detect somebody with an umbrella using different numbers of monitors. Observers viewing one, four, six and nine monitors showed accuracy detection scores of 85%, 74%, 58% and 53% respectively in picking up the person with the umbrella.
By bringing video analytics into the mix, operators get additional detection support. Unlike a human being, machine learning algorithms don’t get tired or lose focus, so they’re able to consistently detect genuine and false alarms for as long as needed.
To ensure intelligent video analytics is as accurate as possible, the key metric to consider is recall: this is how accurate the technology is at capturing genuine alarms. Another metric you might want to measure is reduction - which is the rate of false alarms that have been filtered out. You want both these numbers to be as close to 100% as possible.
3. Continuous learning and improvement
The most exciting thing about AI-powered video analytics is that they are constantly learning, which means they’re always getting better. This is because the more you expose machine learning algorithms to video footage, the more accurate they become at identifying true and false alarms.
This is great news for security businesses who invest in video analytics because the product isn’t static - it actually gets better and better over time.
