Esser Access Control Softwares

While the application of facial recognition within both public and private spheres continues to draw criticism from those who see it as a threat to civil rights, this technology has become extremely commonplace in the lives of iPhone users. It is so prevalent, in fact, that by 2024 it is predicted that 90% of smartphones will use biometric facial recognition hardware. CCTV surveillance cameras  Similarly, CCTV is a well-established security measure that many of us are familiar with, whether through spotting images displayed on screens in shops, hotels and offices, or noticing cameras on the side of buildings. It is therefore necessary we ask the question of why, when facial recognition is integrated with security surveillance technology, does it become such a source of contention? It is not uncommon for concerns to be voiced against innovation. History has taught us that it is human nature to fear the unknown, especially if it seems that it may change life as we know it. Yet technology is an ever-changing, progressive part of the 21st century and it is important we start to shift the narrative away from privacy threats, to the force for good that LFR (Live Facial Recognition) represents. Live Facial Recognition (LFR) We understand the arguments from those that fear the ethics of AI and the data collection within facial recognition Across recent weeks, we have seen pleas from UK organisations to allow better police access to facial recognition technology in order to fight crime. In the US, there are reports that LAPD is the latest police force to be properly regulating its use of facial recognition to aid criminal investigations, which is certainly a step in the right direction. While it is understandable that society fears technology that they do not yet understand, this lack of knowledge is exactly why the narrative needs to shift. We understand the arguments from those that fear the ethics of AI and the data collection within facial recognition, we respect these anxieties. However, it is time to level the playing field of the facial recognition debate and communicate the plethora of benefits it offers society. Facial recognition technology - A force for good Facial recognition technology has already reached such a level of maturity and sophistication that there are huge opportunities for it to be leveraged as a force for good in real-world scenarios. As well as making society safer and more secure, I would go as far to say that LFR is able to save lives. One usage that could have a dramatic effect on reducing stress in people with mental conditions is the ability for facial recognition to identify those with Alzheimer’s. If an older individual is seemingly confused, lost or distressed, cameras could alert local medical centres or police stations of their identity, condition and where they need to go (a home address or a next of kin contact). Granted, this usage would be one that does incorporate a fair bit of personal data, although this information would only be gathered with consent from each individual. Vulnerable people could volunteer their personal data to local watchlists in order to ensure their safety when out in society, as well as to allow quicker resolutions of typically stressful situations. Tracking and finding missing persons Another possibility for real world positives to be drawn from facial recognition is to leverage the technology to help track or find missing persons, a lost child for instance. The most advanced forms of LFR in the market are now able to recognise individuals even if up to 50% of their face is covered and from challenging or oblique angles. Therefore, there is a significant opportunity not only to return people home safely, more quickly, but also reduce police hours spent on analysing CCTV footage. Rapid scanning of images Facial recognition technology can rapidly scan images for a potential match Facial recognition technology can rapidly scan images for a potential match, as a more reliable and less time-consuming option than the human alternative. Freed-up officers could also then work more proactively on the ground, patrolling their local areas and increasing community safety and security twofold. It is important to understand that these facial recognition solutions should not be applied to every criminal case, and the technology must be used responsibly. However, these opportunities to use LFR as force for good are undeniable.   Debunking the myths One of the central concerns around LFR is the breach of privacy that is associated with ‘watchlists’. There is a common misconception, however, that the data of every individual that passes a camera is processed and then stored. The reality is that watch lists are compiled with focus on known criminals, while the general public can continue life as normal. The very best facial recognition will effectively view a stream of blurred faces, until it detects one that it has been programmed to recognise. For example, an individual that has previously shoplifted from a local supermarket may have their biometric data stored, so when they return to that location the employees are alerted to a risk of further crimes being committed. Considering that the cost of crime prevention to retailers in recent years has been around £1 billion, which therefore impacts consumer prices and employee wages, security measures to tackle this issue are very much in the public interest. Most importantly, the average citizen has no need to fear being ‘followed’ by LFR cameras. If data is stored, it is for a maximum of 0.6 seconds before being deleted. Privacy Privacy is ingrained in facial recognition solutions, yet it seems the debate often ignores this side of the story Privacy is ingrained in facial recognition solutions, yet it seems the debate often ignores this side of the story. It is essential we spend more time and effort communicating exactly why watchlists are made, who they are made for and how they are being used, if we want to de-bunk myths and change the narrative. As science and technology professionals, heading up this exciting innovation, we must put transparency and accountability at the centre of what we do. Tony Porter, former Surveillance Camera Commissioner and current CPO at Corsight AI, has previously worked on developing processes that audit and review watch lists. Such restrictions are imperative in order for AI and LFR to be used legally, as well as ethically and responsibly. Biometrics, mask detection and contactless payments Nevertheless, the risks do not outweigh the benefits. Facial recognition should and can be used for good in so many more ways than listed above, including biometric, contactless payments, detecting whether an individual is wearing a facemask and is therefore, safe to enter a building, identifying a domestic abuse perpetrator returning to the scene of a crime and alerting police. There are even opportunities for good that we have not thought of yet. It is therefore not only a waste not to use this technology where we can, prioritising making society a safer place, it is immoral to stand by and let crimes continue while we have effective, reliable mitigation solutions.  

Like most industries, the fields of security, access and safety have been transformed by technology, with AI-driven automation presenting a clear opportunity for players seeking growth and leadership when it comes to innovation. In this respect, these markets know exactly what they want. They require solutions that accurately (without false or negative positives) classify and track people and/or vehicles as well as the precise location and interactions between those objects. They want to have access to accurate data generated by best-of-class solutions irrespective of the sensor modality. And, they need to be able to easily deploy such solutions, at the lowest capex and opex, with the knowledge that they can be integrated with preferred VMSs and PSIMs, be highly reliable, have low install and maintenance overheads and be well supported. With these needs in mind, camera and computer vision technology providers, solutions providers and systems integrators are forging ahead and have created exemplary ecosystems with established partnerships helping to accelerate adoption. At the heart of this are AI and applications of Convolutional neural networks (CNN), an architecture often used in computer vision deep learning algorithms, which are accomplishing tasks that were extremely difficult with traditional software. But what about 3D sensing technologies and perception? The security, safety and access market have an additional crucial need: they must mitigate risk and make investments that deliver for the long-term. This means that if a systems integrator invests in a 3D sensing data perception platform today, it will support their choice of sensors, perception strategies, applications and use cases over time without having to constantly reinvest in alternative computer hardware and perception software each time they adopt new technology or systems. This begs the question - if the security industry knows what it needs, why is it yet to fully embrace 3D sensing modalities? Perception strategy Intelligent perception strategies are yet to evolve which sees designers lock everything down at the design phase Well, one problem facing security, safety and access solutions providers, systems integrators and end-users when deploying first-generation 3D sensing-based solutions is the current approach. Today, intelligent perception strategies have yet to evolve beyond the status quo which sees designers lock everything down at the design phase, including the choice of the sensor(s), off-the-shelf computer hardware and any vendor-specific or 3rd party perception software algorithms and deep learning or artificial intelligence. This approach not only builds in constraints for future use-cases and developments, it hampers the level of perception developed by the machine. Indeed, the data used to develop or train the perception algorithms for security, access and safety use cases at design time is typically captured for a narrow and specific set of scenarios or contexts and are subsequently developed or trained in the lab. Technology gaps As those in this industry know too well, siloed solutions and technology gaps typically block the creation of productive ecosystems and partnerships while lack of commercial whole products can delay market adoption of new innovation. Perception systems architectures today do not support the real-time adaptation of software and computing engines in the field. They remain the same as those selected during the design phase and are fixed for the entire development and the deployment stage. Crucially, this means that the system cannot deal with the unknowns of contextually varying real-time situations where contexts are changing (e.g being able to reflex to security situations they haven’t been trained for) and where the autonomous system’s perception strategies need to dynamically adjust accordingly. Ultimately, traditional strategies have non-scalable and non-adaptable competing computing architectures that were not designed to process the next generation of algorithms, deep learning and artificial intelligence required for 3D sensor mixed workloads. What this means for industries seeking to develop or deploy perception systems, like security, access and safety, is that the available computing architectures are generic and designed for either graphic rendering or data processing. Solutions providers, therefore, have little choice but to promote these architectures heavily into the market. Consequently, the resulting computing techniques are defined by the computing providers and not by the software developers working on behalf of the customer deploying the security solution. Context…. we don’t know what we don’t know Perception platform must have the ability to adjust to changes in context, thereby improving the performance post-deployment To be useful and useable in the security context and others, a perception platform must have the ability to adjust to changes in context, can self-optimise and crucially, can self-learn, thereby improving the performance post-deployment. The combinations of potential contextual changes in a real-life environment, such as an airport or military base, are innumerable, non-deterministic, real-time, often analogue and unpredictable. The moment sensors, edge computing hardware and perception software are deployed in the field, myriad variables such as weather, terrain as well as sensor mounting location and orientation all represent a context shift where the perception systems’ solution is no longer optimal. For example, it might be that a particular sensor system is deployed in an outdoor scenario with heavy foliage. Because the algorithm development or training was completed in the lab, the moving foliage, bushes or low trees and branches are classified as humans or some other false-positive result. Typically, heavy software customisation and onsite support then ensue, requiring on-site support by solutions vendors where each and every sensor configuration needs to be hand-cranked to deliver something that is acceptable to the end customer. A new approach for effective perception strategies Cron AI is building senseEDGE, which represents a significant evolution in the development of sensing to information strategy.  It is a 3D sensing perception and computer vision platform built from the ground up to address and remove the traditional deployment and performance bottlenecks we’ve just described. senseEDGE is aware of the user application reaction plan indication to trigger an alarm or turning on a CCTV camera The entire edge platform is built around a real-time scalable and adaptable computing architecture that’s flexible enough for algorithms and software to scale and adapt to different workloads and contexts. What’s more, it has real-time contextual awareness, which means that the entire edge platform is, at any time, aware of the external context, the sensor and sensor architecture and the requirements of the user application. Furthermore, when it produces the object output data, it also aware of the user application reaction plan indication, which could be triggering an alarm or turning on a CCTV camera when a specific action is detected. This approach turns traditional perception strategies on their head: it is software-defined programmable perception and computing architecture, not hardware-defined. It is free from the constraints imposed by traditional CPU or GPU compute dictated by hardware architecture providers and not limited to the perception built defined during design time. And, being fully configurable, it can be moved from one solution to another, providing computation for different modalities of sensors designed for different use cases or environments, and lower risk of adoption and migration for those developing the security solution.  Future perception requirements senseEDGE is also able to scale to future perception requirements, such as algorithms and workloads produced by future sensors as well as computational techniques and neural networks that have yet to be invented. Meanwhile, latency versus throughput is totally software-defined and not limited by providers of computing architecture. Finally, contextually aware, it is fully connected to the real world where the reflexes adapt to even the subtlest changes in context, which makes all the difference in time and accuracy in critical security situations. This is how CronAI sees the future of perception. It means that security and safety innovators can now access and invest with low risk in a useable and scalable perception solution that can truly take advantage of current and future 3D sensor modalities.

Urban populations are expanding rapidly around the globe, with an expected growth of 1.56 billion by 2040. As the number of people living and working in cities continues to grow, the ability to keep everyone safe is an increasing challenge. However, technology companies are developing products and solutions with these futuristic cities in mind, as the reality is closer than you may think. Solutions that can help to watch over public places and share data insights with city workers and officials are increasingly enabling smart cities to improve the experience and safety of the people who reside there. Rising scope of 5G, AI, IoT and the Cloud The main foundations that underpin smart cities are 5G, Artificial Intelligence (AI), and the Internet of Things (IoT) and the Cloud. Each is equally important, and together, these technologies enable city officials to gather and analyse more detailed insights than ever before. For public safety in particular, having IoT and cloud systems in place will be one of the biggest factors to improving the quality of life for citizens. Smart cities have come a long way in the last few decades, but to truly make a smart city safe, real-time situational awareness and cross-agency collaboration are key areas which must be developed as a priority. Innovative surveillance cameras with integrated IoT Public places need to be safe, whether that is an open park, shopping centre, or the main roads through towns Public places need to be safe, whether that is an open park, shopping centre, or the main roads through towns. From dangerous drivers to terrorist attacks, petty crime on the streets to high profile bank robberies, innovative surveillance cameras with integrated IoT and cloud technologies can go some way to helping respond quickly to, and in some cases even prevent, the most serious incidents. Many existing safety systems in cities rely on aging and in some places legacy technology, such as video surveillance cameras. Many of these also use on-premises systems rather than utilising the benefits of the cloud. Smart programming to deliver greater insights These issues, though not creating a major problem today, do make it more challenging for governments and councils to update their security. Changing every camera in a city is a huge undertaking, but in turn, doing so would enable all cameras to be connected to the cloud, and provide more detailed information which can be analysed by smart programming to deliver greater insights. The physical technologies that are currently present in most urban areas lack the intelligent connectivity, interoperability and integration interfaces that smart cities need. Adopting digital technologies isn’t a luxury, but a necessity. Smart surveillance systems It enables teams to gather data from multiple sources throughout the city in real-time, and be alerted to incidents as soon as they occur. Increased connectivity and collaboration ensures that all teams that need to be aware of a situation are informed instantly. For example, a smart surveillance system can identify when a road accident has occurred. It can not only alert the nearest ambulance to attend the scene, but also the local police force to dispatch officers. An advanced system that can implement road diversions could also close roads around the incident immediately and divert traffic to other routes, keeping everyone moving and avoiding a build-up of vehicles. This is just one example: without digital systems, analysing patterns of vehicle movements to address congestion issues could be compromised, as would the ability to build real-time crime maps and deploy data analytics which make predictive policing and more effective crowd management possible. Cloud-based technologies Cloud-based technologies provide the interoperability, scalability and automation Cloud-based technologies provide the interoperability, scalability and automation that is needed to overcome the limitations of traditional security systems. Using these, smart cities can develop a fully open systems architecture that delivers interoperation with both local and other remote open systems. The intelligence of cloud systems can not only continue to allow for greater insights as technology develops over time, but it can do so with minimal additional infrastructure investment. Smart surveillance in the real world Mexico City has a population of almost 9 million people, but if you include the whole metropolitan area, this number rises sharply to over 21 million in total, making it one of the largest cities on the planet. Seven years ago, the city first introduced its Safe City initiative, and ever since has been developing newer and smarter ways to keep its citizens safe. In particular, its cloud-based security initiative is making a huge impact. Over the past three years, Mexico City has installed 58,000 new video surveillance cameras throughout the city, in public spaces and on transport, all of which are connected to the City’s C5 (Command, Control, Computers, Communications and Citizen Contact) facility. Smart Cities operations The solution enables officers as well as the general public to upload videos via a mobile app to share information quickly, fixed, body-worn and vehicle cameras can also be integrated to provide exceptional insight into the city’s operations. The cloud-based platform can easily be upgraded to include the latest technology innovations such as licence plate reading, behavioural analysis software, video analytics and facial recognition software, which will all continue to bring down crime rates and boost response times to incidents. The right cloud approach Making the shift to cloud-based systems enables smart cities to eliminate dependence on fibre-optic connectivity and take advantage of a variety of Internet and wireless connectivity options that can significantly reduce application and communication infrastructure costs. Smart cities need to be effective in years to come, not just in the present day, or else officials have missed one of the key aspects of a truly smart city. System designers must build technology foundations now that can be easily adapted in the future to support new infrastructure as it becomes available. Open system architecture An open system architecture will also be vital for smart cities to enhance their operations For example, this could include opting for a true cloud application that can support cloud-managed local devices and automate their management. An open system architecture will also be vital for smart cities to enhance their operations and deliver additional value-add services to citizens as greater capabilities become possible in the years to come. The advances today in cloud and IoT technologies are rapid, and city officials and authorities have more options now to develop their smart cities than ever before and crucially, to use these innovations to improve public safety. New safety features Though implementing these cloud-based systems now requires investment, as new safety features are designed, there will be lower costs and challenges associated with introducing these because the basic infrastructure will already exist. Whether that’s gunshot detection or enabling the sharing of video infrastructure and data across multiple agencies in real time, smart video surveillance on cloud-based systems can bring a wealth of the new opportunities.