It costs a lot of money to make sure the law is followed. According to the Urban Institute, the United States spends $123 billion per year on policing the streets.
Many private and public organizations are now using artificial intelligence technology to explore new ways to find and stop crimes.
Some AI software determines where crimes are most likely to happen based on information from past crimes, while others use technology to identify crimes happening in real time and others that utilize face and vehicle recognition to find suspected criminals.
These solutions leverage machine learning and advancements in computer-vision to listen, see, detect and report patterns that may require public safety attention.
Computer vision, also called "machine vision," helps AI recognize and classify what it sees. The technology also allows businesses and government agencies to make long-term decisions using AI's ability to spot patterns and high-level analytical skills.
Face recognition and inventory tracking are two of the most common ways computer vision is already used in retail, healthcare, and manufacturing, among other fields.
Face Recognition in Crowd
In the past few years, smart cities have emerged across the globe, and they are expected to proliferate in the near future. These cities use IoT, AI, and data sensors to improve traffic management, transportation, water and waste management, IT, educational institutions, libraries, healthcare services, and other more systematic and coordinated activities. While smart cities have many objectives that benefit society some of the results to date have been related to use of smart technologies for aiding law enforcement.
Here are some examples of how computer vision may help law enforcement officers all across the globe:
Technology has the potential to play a similar role in law enforcement. Smart cities use several cameras and sensors to provide law enforcement personnel with a constant stream of data. Consequently, computer vision allows such specialists to respond quickly. Computer vision enhances machine learning by recording and analyzing visual data for subsequent analysis. The most significant use of computer vision in law enforcement, however, may be the prediction of crimes before they occur. Some movies, like Steven Spielberg's Minority Report, which was a big hit in 2002, looked at the idea of stopping people from committing crimes before they happened. In the real world, however, computer vision forecasts crimes using historical data and AI pattern recognition, among other methods.
Computer vision-based crime prediction systems identify, prevent, and resolve crimes more quickly using statistical observations of crimes in a given region. Machine learning is predicated on the premise that crimes may be anticipated based on certain underlying circumstances that have previously resulted in crimes. Computer vision may help with predictive analysis by detecting 3D objects and pedestrians, enhanced face recognition, location recognition, and differentiating between them.
Naturally, crime forecasting is still in its early stages and is not extensively used. The United States, the United Kingdom, the Netherlands, Germany, and Switzerland are among the nations experimenting with this strategy in their law enforcement activities today.
Drones offer law enforcement officials viable alternatives for crowd control and unmanned traffic regulation. Drones may be equipped with computer vision technologies to improve their image-capturing and analysis capabilities. The primary use of computer vision in smart cities and drones enables law enforcement agencies to do real-time image and video analytics.
However, several concerns may prevent the use of drones for public monitoring. First and foremost, the drones must be deployed in such a way that civilian privacy is not jeopardized. To satisfy this criterion, countries may enact regulations to guarantee that drones are exclusively used for search and rescue activities, accident reporting, disaster management, and emergency assistance.
Several nations use drones for police enforcement. The Netherlands, Spain, and Germany are among the most notable. Drones are used for surveillance in several European Union nations while strictly governed by the EU's strong GDPR data protection and privacy rules. The surveillance limitation provides transparency in the application's use.
Drones could be valuable law enforcement allies for field personnel since they can communicate information to police headquarters if the machine vision detects that an officer needs a break during or after engaging in severe anti-criminal activities.
In most countries, these are arguably the most commonly accepted uses of computer vision in smart cities and law enforcement. Organizations and governmental bodies in smart cities are already using face recognition technologies to avoid fraud and warn police officers if individuals of interest are identified. However, when it comes to the use of computer vision in smart cities and law enforcement, these technologies are just the beginning and will spark more advanced crime fighting applications.
In law enforcement, facial recognition may be used to monitor crime suspects in the open, apprehend lawful criminals, and convict criminals by obtaining video evidence. The technology is helpful for lie-detector tests since computer vision components in such systems can analyze interrogation subjects' emotional reactions and facial movements to determine whether they are perpetrators (or eyewitnesses) in criminal cases or not.
As we know, 'experienced' machine learning algorithms may rely on billions of documents of previous data to judge a subject's behavior and notify interrogators whether their target is guilty (or hiding information) from them or not. Such cooperation may be crucial to law enforcement personnel since it enables them to delve deeper into evidence against such people.
In addition to interrogating detainees, face recognition technology may be utilized to track questionable individuals involved in illegal activities. Finding someone who is skilled at avoiding detection after committing a crime may be difficult for law enforcement agents. However, improved face recognition may detect such persons even if they try blending in with a sea of people in densely populated regions. Data specialists in law enforcement agencies give face recognition systems with images of such persons for machine training.
The computer vision aspect of a facial recognition system may produce a 3D representation of the individual's face and general look based on pictorial and video-based data. What else? To evade identification, the technology works independently of backdrop illumination, identity-concealing disguises, or facial hair growth. Because such an application would need high levels of connection and continuous data flow, smart cities are an excellent environment to deploy it. Sensors and cameras in smart cities can hunt down criminals and harmful persons, even in poor lighting and visibility.
In addition to facial recognition, computer vision allows such systems to carefully check and compare faces, making the task of law enforcement officials easier. Facial validation may be more beneficial since it tells officers whether a detained individual is innocent or guilty (based on their criminal record) and if the individual sought by the police and the one taken into custody are the same.
Perhaps more intriguingly, one of its primary uses is to inform law enforcement personnel and general face recognition systems if the person on the other side of a screen is a genuine person or a bot. As you may have seen, numerous websites always require new account creators to confirm that they are not robots. Fake picture identification may be very difficult for law enforcement organizations since it leads them to pursue faceless shadows throughout the internet.
Consequently, computer vision's face recognition and verification are extremely valuable for police officers and other law enforcement personnel globally.
Smart city governments undoubtedly provide their inhabitants with the highest level of physical protection. However, this security comes at the expense of residents' privacy. Public officials continually watch city residents get the data needed to train technology systems. Citizens are often unaware of how their data is processed, what it is used for, and where it is kept. They are also unsure whether or not their data will be protected. Citizens' anxiety about data security comes at a cost for physical safety. Physical safety is critical, but data privacy is also important.
Data breaches may cost governments millions of dollars. More than ten government data breaches have affected millions of individuals in the United States alone. Data breaches enable cybersecurity attackers to access people's personal information, which they may use against them. And, as the amount of data collected to offer the finest services grows, so does the number of cyber attack events.
We constantly hear about cybersecurity assaults and breaches that result in massive data loss. Data breaches may leak sensitive information, transactional data, or intellectual property, depending on the volume and kind of data involved. As a result, in such a case, governments of smart cities should prioritize data security alongside physical security.
Data is precious, and it becomes more valuable when it's collected over time. Organizations keep video insights securely for an extended time to derive value from them. Yet, law enforcers and other entities that obtain footage from third parties must protect bystanders. It, therefore, goes without saying that smart city footage, small business owners, and police need to be dedicated to privacy rules.
Sighthound is one of the few providers of video redaction technology, making it possible to automatically remove or obscure details like faces, cars, and license plates from digital media. Redacted videos help businesses meet regulatory standards and extrapolate insights that promote revenue growth or market intelligence and allow it to be stored for extended periods while still meeting regulatory standards like GDPR. The goal is to help organizations obtain meaningful insights from video data while still keeping bystanders safe.
Redacted Face at Rail Terminal
Sighthound is committed to supporting today and tomorrow’s privacy needs with our software suite that includes computer vision software that identifies people or objects in video, tracking their movement, and supporting privacy with object redaction. To learn more about finding objects in videos head to www.sighthound.com. To start redacting private information in videos, check out the free trial at www.redactor.com/free-trial.