When the US Air Force recently shot down three mysterious objects near North America people were understandably worried. Many speculated that these could be potential threats spying on critical US infrastructure. And although it later turned out that they were just harmless balloons this incident underscores the challenges we face with object identification in 2023. In a world where even the most advanced military-grade camera systems are having a hard time identifying a research balloon, it is no wonder that false alarms occur with CCTV cameras.
False security alarms refer to alarms triggered by non-threatening objects or circumstances. It occurs when a security camera categorizes a non-threatening thing or an incident as a trigger. A recent study found that 41% of businesses experienced false security alarms due to insects, dirt, and other types of obstructions.
While one might expect that the issue of false alarms would be resolved by 2023, this is far from the truth. In reality, false security alarms may pose an even greater challenge for businesses in the upcoming years due to the following aggregating factors.
False security alarms place additional financial stress on any organization, causing security personnel to waste valuable time on insignificant incidents and distracting other employees from delivering high-quality output. As a result, some businesses are scaling back their investments in surveillance systems that lack high accuracy. Meanwhile, other companies are actively seeking alternative suppliers that offer similar services at reduced prices. For professional security firms, this implies that their clients may request price reductions or even consider transitioning to rival companies in 2023. In a challenging environment like this, only reasonably priced remote video monitoring offering with precise false alarm filtering capabilities can be widely adopted by businesses.
Fortunately, AI can enhance the efficiency of the existing CCTV infrastructure, remove up to 95% of all false alarms and increase profit margins by reducing the reliance on manual alarm filtering. The lingering question, however, is how to seamlessly integrate this cutting-edge AI technology into the current infrastructure. One obvious option is investing in cameras with built-in AI capabilities. While these cameras are steadily gaining traction in the consumer market, a common challenge businesses face is finding the perfect balance between cost and accuracy. The market is flooded with options, ranging from high-end, AI-optimized cameras to more budget-friendly alternatives that promise an array of AI features. Navigating this landscape and identifying the most suitable solution for your business can be a complex and daunting task. Here are the factors that contribute to this cost vs. accuracy dilemma and offer insights on how to make the best decision for your organization.
There's no denying that AI-powered cameras with superior accuracy and advanced analytics capabilities come at a premium price. These high-end cameras can set you back around $2500 per unit, a significant investment for any business. The reason behind the hefty price is the use of advanced computation modules, like the Nvidia Jetson NX. These modules allow manufacturers to achieve unparalleled accuracy in detecting security threats and minimizing false alarms. While these cameras may be worth the investment for some organizations, many businesses, especially small and medium-sized enterprises, may find it difficult to justify such a significant expense.
This is where affordable AI cameras, with a price tag of around $500, enter the picture. On the surface, affordable AI cameras seem like a perfect solution for businesses looking to implement AI-driven security without breaking the bank. However, there's a catch: these cameras often suffer from lower accuracy rates and limited analytics capabilities compared to their high-end counterparts. As a result, they may struggle to detect genuine threats and frequently generate false security alarms, leading to increased operational costs and reduced efficiency. The reason behind this performance gap lies in the inherent limitations of these budget-friendly cameras. To keep costs low, manufacturers often compromise on the computational power that is needed to run the latest AI algorithms.
As the AI-powered camera market continues to evolve, the gap between high-end and affordable cameras is becoming more pronounced. Premium cameras are becoming increasingly accurate and expensive, while affordable options are becoming cheaper but struggle to improve accuracy in specific use cases.
If a false security alarm is triggered and police officers are sent the business may face fines for waste of public resources. This scenario may occur when such a false security alarm is set off at night. In the past couple of years, more and more US states and countries introduced fines for false security alarms. In Los Angeles, for instance, you may be charged 5,000 $ for 10 false alarms within a year. In San Jose, this amount is around 6,000 $. One important take from these fines is that amount of fine imposed on a business increase as more false alarms are set off. Therefore, fewer false alarms equal fewer fines.
Since the pandemic, more and more businesses have switched to the Cloud because it helps to reduce costs on many levels. For instance, migrating to the cloud may reduce the cost of video storage infrastructure by 20 to 50%. Removing local NVRs can also save businesses from the maintenance work that they would otherwise have to do on their premises, and make infrastructure more resilient
While economic efficiencies motivated businesses to adopt Cloud, they also discovered other vital benefits they can reap by migrating to the cloud. AI video analytics was one of the most prominent ones. According to one Report, 51% of businesses surveyed implemented AI-powered video processing technology in 2022. In the same report, almost half of the companies stated that they were reluctant to implement AI-based video analytics technology. 47% of those businesses cited upfront costs as the primary reason for not implementing AI technologies. More interestingly, 30% of them referred to “lack of accuracy” as the primary reason. Does this sound familiar to you?
These two major concerns usually occur when businesses deploy security cameras with built-in AI. As we have addressed above, premium CCTV cameras with accurate AI capability are costly investments due to the high price tag of semiconductors. Cheaper alternatives lack capable processors to run advanced AI onboard the camera.
On the contrary, cloud-based AI does not require a huge upfront investment and can work with existing cameras, even if they are decade old. Cloud-based AI is also more accurate because AI algorithms are hosted on powerful servers that are not constrained by limited power supply or heat dissipation problems. Furthermore, AI Cloud deployment enables businesses to benefit from the AI’s ability to learn and improve over time. By feeding on a constant influx of new video data, the Cloud AI algorithm can work wonders with its accuracy levels and reduce the rate of false security alarms to negligible levels.
While the market may not be ready to offer affordable AI cameras with top-tier performance anytime soon, the popularity of cost-effective, high-resolution cameras has surged significantly. High-resolution IP cameras have become more popular in the past few years as their prices went down. These cameras enable businesses to capture higher-quality data, which in turn enhances the efficiency of Cloud AI algorithms. Additionally, these cameras typically perform better in low-light conditions, further contributing to improved accuracy rates.
In 2023, businesses face more risks due to false alarms in their security systems, causing financial stress and resource wastage. Economic uncertainties and increasing fines for false alarms have made it crucial for organizations to find accurate and cost-effective solutions. AI-powered cameras offer a promising answer to this problem, but the dilemma between cost and accuracy remains. As the market evolves, these key trends will likely shape the future of video security:
- The use of Cloud-based AI video analytics solutions will become more popular.
- Migrating to the cloud will enable businesses to reap the benefits of AI video analytics and will help reduce operational costs
- High-resolution IP cameras will remain a popular choice.
Curious about how you can achieve the lowest false alarm rates possible?
Contact us to talk about your false alarm problems and how we can help.
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