Law enforcement agencies are increasingly relying on AI-driven video analytics to process surveillance footage — a technology which is proving to be both powerful for solving crimes and is subject to scrutiny and controversy
Law enforcement is relying more heavily on video evidence, submitted both from community members and by analyzing data from public and privately owned security cameras. As police departments nationwide wade through thousands of hours of video, they are increasingly relying on AI-trained video analytics solutions to decipher data more effectively and more quickly.
The technological evolution of data-driven policing
Aggregating data from multiple sources to more effectively allocate police resources is not a novel concept. Indeed, more than 30 years ago, the New York City Police Department launched CompStat, its constantly updated database of daily crimes.
The core practices of aggregating data and generating visualizations to inform predictive policing have been duplicated across more than 100 law enforcement agencies in the United States and are practiced in real-time crime centers of contemporary policing. Data-driven policing can help short-staffed agencies and foster greater community trust and engagement through informed outreach programs and data dashboards.
Historically CompStat and other such programs relied on data from previous crimes to allocate resources and predict future crimes. New York City led American cities in investing aggressively in closed-circuit camera surveillance with its Domain Awareness System more than a decade ago. Video analytics were a key factor in helping to quickly identify two suspects in the aftermath of the Boston Marathon Bombing in 2013.
Other contemporary technology such as body-worn cameras, license plate readers, gunshot detectors, internet-connected private security cameras (shared voluntarily with law enforcement departments) and facial recognition technology allow departments to track criminal activity in near-real time. A surge of post-pandemic American Rescue Plan Act (ARPA) dollars have funded law enforcement departments with license plate readers, video analytics tools, and security camera investments that serve as force multipliers to help short-staffed law enforcement agencies to monitor their communities.
Most recently, in the aftermath of the murder of United Healthcare CEO Brian Thompson in New York City in December 2024, video analytics and AI facial recognition technology were utilized to help track a suspect’s path within New York City and then widely distribute security camera footage of the suspect’s face. The suspect fled the crime scene but could not escape detection by the network of thousands of cameras monitoring New York City.
How AI tools make hours of video content functional
The volume of video captured across these interconnected tools would never be manageable for real-time human review. AI incorporates behavior analysis in complex environments to flag anomalies for human review — finding the needle in the digital haystack. Algorithms are either trained on a specific behavior (such as identifying if a person enters a secure area, or if a suspicious item is left behind) or are programmed with a learning algorithm which can adjusted based on past behaviors. A learning algorithm would, for example, be able to identify the difference between the movement of a bag blowing across a parking lot compared to a human being in the same parking lot.
Video analytics companies can review body-worn camera footage and recognize instances of police professionalism and thus, enhance many citizens’ interaction with police. Law enforcement agencies generally review an inordinately small amount of body-worn camera footage due to resource constraints, but analytics tools which automatically detect critical events (such as use of force, apprehensions, de-escalation attempts) can help identify areas that call for enhanced training or could improve citizens’ experience with law enforcement.
Of course, facial recognition technology and its use by law enforcement agencies has been a hotly debated topic since the 2020 aftermath of the death of George Floyd. The technology has been scrutinized for alleged inappropriate use by law enforcement agencies and concerns that the tool has poor accuracy ratings in recognizing black and brown faces. In fact, the State of Maryland adopted legislation regulating law enforcement’s use of facial recognition technology this past year, and Maryland State Police adopted a model policy around the technology, in accordance with the new state law.
Further, new policies and legislation commonly ensure that facial recognition technology is not used alone to establish probable cause or surveil Constitutionally protected activities, and there are additional concerns about protecting the identity of minors as well.
Addressing staffing shortages through more effective policing methods
Video analytics and other data-driven technologies can aid departments in addressing criminal activities more efficiently, especially if their agency is short-staffed. For example, Seattle invested heavily in police surveillance equipment in three high-activity corridors last year and installed license plate readers on all police department vehicles. This is concurrent with the police department indicating that they will not respond to tripped alarm systems calls without additional verification, such as from video, audio, or eyewitness, for example.)
The Seattle Police Department has struggled to address a nearly 30% vacancy rate since the recent global pandemic and must prioritize their respond to calls as responding to all in a timely fashion is not possible. The intersection of technology in policing and staffing shortages has cracked open the door on discussing when calling 911 is appropriate and if 24/7 alternative response programs might work better than traditional law enforcement responses.
Law enforcement agencies can use video analytics and other emerging technologies to respond to crime in a more resource-conscious and efficient manner. These efficiencies must balance public desire for transparency and collaboration in how these technologies are deployed, how privacy is protected, and how data is secured. Also, policy measures should be implemented to ensure the utilization of the most current technologies, and efforts also should be made to train AI algorithms on unbiased data sets to prevent any perpetuation of harm against marginalized communities.
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