New AI Tracking Tech Enables Police to Bypass Facial Recognition Bans

Law enforcement agencies across the United States are adopting a new artificial intelligence tool called Track, developed by Veritone, that enables them to monitor and track individuals without relying on facial recognition technology. This innovation comes amid growing legal restrictions on facial recognition use, especially in states and cities that have enacted bans or stringent regulations to protect civil liberties.

Unlike traditional facial recognition systems, Track identifies people based on a combination of physical and contextual attributes such as body size, gender, hair color and style, clothing, and accessories. This allows agencies to track individuals even when faces are obscured or not visible, effectively circumventing laws that restrict biometric facial data use.

Currently, Track operates on recorded video footage from diverse sources including police body cameras, drones, public surveillance, and citizen-uploaded videos from platforms like YouTube and Ring cameras. The company plans to enable live video feed analysis within a year, which would significantly enhance real-time surveillance capabilities.

Veritone’s CEO Ryan Steelberg describes Track as a tool to assist law enforcement in identifying criminals or malicious activity when facial recognition is legally restricted or faces are not visible. The tool assembles timelines to track individuals across multiple locations and video feeds, providing a powerful investigative resource.

However, the technology has sparked significant concern from civil liberties advocates, including the American Civil Liberties Union (ACLU). They warn that Track raises many of the same privacy issues as facial recognition but also introduces new risks by enabling large-scale, non-biometric tracking. This could lead to invasive surveillance practices, especially targeting protesters, immigrants, and students amid federal efforts to increase monitoring.

The ACLU highlights that Track’s ability to track individuals based on mutable attributes like clothing or accessories blurs the line between biometric and non-biometric data, potentially sidestepping legal definitions that protect privacy. For example, someone wearing the same coat and backpack daily could be continuously tracked across various video feeds, effectively mirroring facial recognition’s surveillance power.

The expansion of Track’s use is notable within federal agencies such as the Department of Justice and Department of Homeland Security, which have been increasing surveillance on social media and public activities related to immigration and protests. This raises concerns about potential abuses and authoritarian overreach, as the technology enables unprecedented levels of monitoring.

Veritone emphasizes that Track is designed as a 'culling tool' to help law enforcement efficiently analyze large volumes of video footage, not as a general surveillance system. Nevertheless, privacy advocates argue that the scale and nature of tracking enabled by Track represent a new frontier in surveillance that demands careful legal and ethical scrutiny.

As facial recognition laws continue to spread, technologies like Track highlight the challenges regulators face in defining and controlling emerging AI-driven surveillance tools. The debate underscores the urgent need for clear policies that balance public safety with civil liberties in an era of rapidly advancing artificial intelligence.