Crowdsourced mapping tool DeFlock Maps now tracks nearly 129,000 automated license plate readers across the United States, allowing residents to view nearby surveillance devices. The surge in adoption of Flock Safety cameras has sparked community pushback and privacy concerns over continuous vehicle tracking.
Tracking the Rapid Expansion of Automated License Plate Readers
Surveillance infrastructure is proliferating across American neighborhoods, often arriving with little public notice or formal debate. In towns and cities nationwide, automated license plate readers have quietly transformed ordinary streets into continuous tracking zones. Public estimates indicate that roughly 90,000 Flock-branded cameras now operate across the United States, while the company states that its nationwide network encompasses more than 120,000 cameras deployed across 49 states.
These devices are frequently mounted on utility poles beside roads and intersections, where they are easy for motorists to overlook or mistake for standard traffic equipment. Most units operate as small black boxes powered by batteries or solar panels, communicating via cellular networks rather than local Wi-Fi. While police departments represent primary users of the technology, homeowners associations, businesses, and schools also purchase and install the devices.
How the Surveillance Network Captures and Analyzes Movement
Unlike traditional traffic enforcement devices, Falcon cameras manufactured by Flock do not detect vehicle speed. Instead, they capture approximately six to 12 still images each time a car passes. The system then utilizes automated license plate recognition software and machine learning models to process the visual data.
The software analyzes images of vehicles and extracts characteristics such as license plates . . . and other distinguishing features,
said Trevor Horwitz, co-founder and chief information security officer of the cybersecurity firm TrustNet, in an interview.
Those extracted characteristics transform passing automobiles into searchable digital records. Investigators can query the database for a vehicle’s make, model, color, body type, direction of travel, and specific physical identifiers such as aftermarket wheels, damage, bumper stickers, or dents, even when the complete license plate number remains unknown. The roadside units transmit all captured images over cellular networks to a cloud system, where authorized users search the records through the Flock Safety dashboard. Law enforcement agencies receive automated alerts whenever a camera detects a plate matched to a stolen vehicle, an active arrest warrant, or a missing person database.
DeFlock Maps and the Push for Surveillance Transparency
As these camera networks grow, citizens and privacy advocates are pushing back against widespread monitoring. A crowdsourced mapping initiative known as DeFlock Maps aims to provide surveillance transparency by pinpointing automated license plate readers on an open map. Powered by the OpenStreetMap project, the platform relies entirely on contributions from volunteers.
“Camera locations come from volunteers and the OpenStreetMap community, not a private company or government agency.”
The tracking platform currently monitors 128,988 devices, with 82 percent identified as Flock Safety equipment, translating to 105,770 pinned Flock cameras. The remainder includes devices owned by Motorola Solutions, Genetec, and other tech companies. DeFlock states in its user guide that no personal user data is gathered during the mapping process, noting that everything stays on your device except what you choose to upload to OpenStreetMap.
Community Resistance and the Risks of Cumulative Tracking
The expansion of automated tracking has triggered local friction. In Saranac Lake, a New York village, resident protests last spring prompted officials to reconsider the technology, ultimately leading the village to vote to cancel its contract with Flock Safety and remove the cameras. Similar friction has emerged elsewhere; in one Minnesota municipality, citizens stole all of the installed devices in an extreme display of local opposition.
Legal and security experts emphasize that while an individual camera capture may seem innocuous, the aggregation of data across an entire regional network creates profound privacy implications.
When multiple cameras track a motorist near residences, workplaces, schools, and commercial districts, the resulting data paints an intimate portrait of daily routines, associations, and personal habits. Software integration with platforms like Motorola’s CommandCentral Aware further broadens investigative access by combining data feeds across distinct manufacturer networks, giving agencies a deeper pool of investigative leads while deepening concerns over continuous civic monitoring.
