Although the reasons why are unclear, U.S. cities have begun to see an increase in murder rates after a long period of decline. According to the New York Times, which broke the story in August of 2015, 30 cities, including New York, Baltimore, Washington, New Orleans, and St. Louis, have reported sharp spikes in violence over the past year. Additionally, a recent FBI report identified 160 “active shooter” incidents that have occurred between 2000 and 2013, including the shootings at Aurora, Fort Hood, Newtown, Virginia Tech and the Washington Navy Yard.
As if that were not enough, the FBI also has to deal with the growing terror threat represented by the Islamic State and its recruitment efforts within our own borders. In a recent interview, FBI Director James Comey said the Bureau has investigations active in all 50 states to counter the group’s persuasive social media campaign. All too often these days, our law enforcement and security agencies are discovering that the real danger of terrorism is represented by homegrown “lone wolf” operators that have no relationship to the Islamic State other than a shared ideology.
It should therefore come as no surprise that more and more cities are turning to the state of the art technologies of video surveillance to help the authorities identify and track individuals suspected of criminal or terrorist activities. In fact, many cities, such as New York, Chicago, and St. Louis, have implemented “Real Time Crime Centers” consisting of centrally controlled and monitored networks of fixed-location surveillance cameras placed strategically throughout their cities. Often these systems feature advanced video analytics capabilities to help their human operators identify potential threats they would otherwise miss. One of the most effective technologies for this type of application is Automatic License Plate Recognition (ALPR).
In security and law enforcement circles, ALPR is already well known as a useful anti-crime tool. However, it is typically thought of as a mobile system mounted on patrol vehicles. This type of ALPR solution has its uses, but the technology is actually of far greater value as a cornerstone of a fixed-location video surveillance network. Mobile ALPR, even when deployed on a large number of vehicles (which does not usually happen), still cannot cover a city’s streets and important locales as completely and as consistently as a network of fixed cameras can. This kind of comprehensive mass deployment is the only way that ALPR’s potential is truly maximized. In this scenario, every vehicle that passes any camera in the city has its license plate captured and instantly compared to law enforcement databases in real time for active wants or warrants. If any are found, nearby units can be instantly dispatched to intercept them. The responding officers do not have to worry about operating the equipment themselves or interpreting the results, and real-time updates can be continually fed to them via radio as they close in on the suspect—even if they have to get out of their car. Additionally, all plate captures are stored for future reference if a particular vehicle is ever flagged for criminal activity. In that case, the stored data enables police to track the location of the suspect vehicle going back days, weeks, or even months.
It is important to note that a fixed-camera network with ALPR-based video analytics also performs well on a smaller scale, in any location where physical security is needed. Some of the best examples include hospitals and university campuses, as they are very much like small cities themselves. In the latter case, the consistent real-time knowledge of who is entering and leaving the campus and when is of paramount importance to security and/or law enforcement personnel. With a complete network of the kind mentioned above, however, it also becomes possible to know where vehicles are on campus and at what times. The system can also monitor parking and tell you if anyone is parking in a lot for which they are not authorized. Advanced pattern recognition algorithms, available as part of the on-board analytics, can also work in conjunction with perimeter-mounted cameras and alert security personnel if a vehicle is exhibiting suspicious movement patterns around the campus.
In the case of hospitals, the same principles apply. An ALPR-based video analytics solution would provide the ability to intelligently monitor vehicles entering and exiting the property with a minimum of human intervention. It can also enhance monitoring of parking areas, automatically counting vehicles in each area and alerting designated personnel when capacity is reached. Additionally, it can be configured to control access to each parking area purely based on license plates, eliminating the need for ID cards or decals and accurately ensuring that staff, patients, and visitors park in the appropriate areas. Just as importantly, ALPR-based video analytics can alleviate a problem common to many hospitals—the situation where a sick or injured person is quickly dropped off at the Emergency Room from a vehicle that subsequently speeds away. The license plate number that security officers might miss is captured by the ALPR system, complete with full-color images and date and time stamps for law enforcement follow-up.
As ALPR evolves from a primarily mobile to a primarily fixed-location solution, it is also making a dramatic paradigm shift from proprietary hardware-based systems to more flexible and affordable software-based technology. Until very recently, any kind of ALPR solution was dependent on specialized cameras and dedicated processing hardware to function; you also had to buy your entire package from one company. Now, software solutions are available that can perform ALPR with practically any existing camera—even cameras that you may already have in place, regardless of what video management system you may be using. This new approach to the technology has been a significant improvement in cost even for mobile solutions. For fixed solutions, it has been absolutely revolutionary, reducing the cost per camera feed by as much as 70 percent over older systems.
Cost, however, is not the only advantage that this new-school approach provides. Installation time is also dramatically decreased, particularly if there are already surveillance cameras in place; it is simply a matter of installing the software and running simple calibration routines on the desired cameras. The system is flexible enough that you can select only certain cameras among your entire network to perform ALPR; furthermore, you can switch to different cameras at will. Additionally, the hardware-agnostic solution does not rely on infrared (IR) imaging as traditional ALPR does; rather, it processes all of its images in full color. This not only means more picture data for vehicle identification, but also enables the implementation of analytics that recognize vehicle color and make in addition to license plate.
The software-based approach to ALPR and video analytics also enables the technology to be offered in a cloud-based or Software-as-a-Service (SaaS) configuration. All the end user requires is a camera or cameras with a broadband Internet connection. All ALPR and analytic processing is handled by the provider, with the data made available to the user via a customized, password-protected website. This data is secured in such a way that only the user can access it; access is blocked even for the provider.
Unfortunately, there is a great deal of misleading information regarding ALPR in the public sphere. A number of civil rights and privacy advocates have chosen to take a dim view of the technology based on what seems to be a fundamental misunderstanding of its capabilities. Put simply, these groups would have the public believe that ALPR is a violation of the Fourth Amendment to the U.S Constitution because it captures and stores the locations of license plates and can track those locations over time.
Supposedly, this results in a “Big Brother” situation where law enforcement is capable of knowing where any motorist is and where they are going at any time. It is true that ALPR is a powerful technology; that is why it has been so effective in combatting crime and terrorism during the course of its existence. However, there are three basic points about ALPR and its relationship to privacy that public officials should understand:
United States Case Law has already established that the Fourth Amendment’s search and seizure provisions do not extend to a license plate in a public place. Put simply, there is nothing unconstitutional about law enforcement officers photographing license plates in public areas, or even recording the date, time, and location the photograph was taken. One of the best examples of this is the case of U.S. vs. Ellison from 2006. As to ALPR’s ability to compare license plate numbers to law enforcement databases for active wants or warrants, this is nothing new—police have been able to do so manually for decades. The fact that ALPR can do this faster and in greater numbers does not alter the fact that it is a well-established prerogative of law enforcement.
The Police couldn’t be Big Brother even if they wanted to. What so many privacy advocates fail to do in this debate is consider the logic of their position. Yes, it is true that an ALPR database can store license plate capture data for thousands, millions, or even billions of vehicles going back weeks, months, or years. But such a system has the defects of its virtues; in other words, it produces far more data than an organization of human beings would ever have the time or manpower to make use of. The only practical application for such a database is in researching the history of a vehicle that there is already reason to suspect as being used in criminal activity. In short, if you are not breaking any laws, the police are not going to bother to look up any data on you. They don’t have the time.
Terrorists aren’t concerned with privacy issues. The fact is we live in a dangerous time. Our nation, and indeed our world, lives under constant threat of terrorist violence from very powerful organizations such as the Islamic State. In the 21st Century, such organizations have found even more nefarious ways of penetrating our borders than they ever had previously; namely, they use the Internet, particularly social media, to recruit more followers to their cause. As noted above, these are often what are known as “lone wolf” operators; people who appear seemingly out of nowhere and act completely alone in perpetrating heinous acts of violence. What this means is that the enemy could be anywhere. It also means that those who would harm us will use any technological weapon they can find against us. We must be prepared to do the same. ALPR-based video analytics can be one of the most effective tools in this fight. We hesitate to use it at our own peril.
What the Real-Time Crime Centers of St. Louis and other cities are demonstrating is that in the world of video surveillance, mass deployment is the key. The more cameras there are, the more complete the coverage is. In the world of ALPR-based video analytics, this is especially true, as multiple camera placements not only increase the chances of capturing an offending vehicle, but also give authorities the ability to flag such a vehicle and follow its track if it attempts to escape. Beyond this, however, mass deployment also allows for wide collaborative networks of data sharing, whether between cities, between city and state law enforcement, between state and Federal agencies, or among any other combination of agencies one could conceive. The wider this network is, the harder it is for criminals and terrorists to move about undetected. The harder it is for them, the safer the rest of us are.
About the Author:
John Chigos is the visionary founder and CEO of PlateSmart Technologies, a leading industry developer of video analytics security technology. His goal was to develop a camera-agnostic license plate capture solution that incorporates a data analytics backbone for both security/law enforcement and business intelligence uses. Since 9/11, Chigos has established himself as an expert in LPR and video analytics in the areas of terrorism and counter-terrorism.
This article is published in Security Technology Executive.