Tag Archives: cameras

Flock Safety Cameras Promise Safer Streets, Why Are US Communities Pushing Back?

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Automated cameras designed to help police solve crimes are becoming a contentious issue in American communities, exposing a difficult trade-off between public safety and the right to move through public spaces without persistent tracking.

Flock Safety, a major provider of AI-powered surveillance cameras and automated license-plate readers, has deployed approximately 120,000 cameras across 49 states. Its technology is used by more than 4,800 law enforcement agencies and nearly 1,000 businesses. But growing resistance from residents, local officials and privacy advocates is challenging the assumption that more surveillance automatically makes communities safer.

The opposition is increasingly visible in city council decisions, contract reviews and legal challenges. It also raises questions about how much public support exists for a surveillance system that can make vehicle movements searchable across time and locations.

Public support is far from universal

A Reuters/Ipsos poll reported that 38% of respondents supported the use of Flock cameras in their community, while 47% opposed it. The figures indicate that opposition outweighed support among those surveyed, although they do not establish why every respondent held a particular view.

Critics argue that the technology can create a detailed record of people’s movements, even when those individuals are not suspected of a crime. They are concerned about how long records remain searchable, which agencies can access them and whether information can be shared beyond the jurisdiction that collected it.

Those concerns have become more politically sensitive amid reports that surveillance data has been used in immigration enforcement. Privacy advocates say safeguards must account not only for a system’s intended purpose but also for how information may be used by other agencies.

Flock has defended its technology as a tool for public safety and criminal investigations. License-plate readers can help police identify stolen vehicles, locate missing people and develop leads in investigations. The company says its systems include controls intended to protect customer data and restrict access.

The dispute is therefore not simply between supporters of law enforcement and opponents of technology. It concerns what rules should govern the collection and use of data when surveillance systems operate across large areas.

Local governments face pressure to set limits

The debate is producing concrete policy consequences. Florida barred automated license-plate readers from state highways in September, citing privacy risks. In Virginia, a lawsuit has challenged surveillance practices on privacy grounds.

Other local authorities are reconsidering their contracts or examining alternative providers. In South Windsor, Connecticut, the town council voted to end its Flock contract and require the removal of its stationary cameras and deletion of previously collected data.

These decisions reflect a growing demand for clearer accountability. Residents and elected officials want to know whether the technology is delivering measurable results, whether searches are properly authorised and whether police departments can demonstrate that the system is being used appropriately.

The central policy challenge is to distinguish targeted investigation from routine, searchable monitoring. A camera that helps identify a vehicle after a serious crime may have clear value. A system that permits broad searches of ordinary travel patterns raises different questions about proportionality and oversight.

Possible safeguards include short data-retention periods, documented reasons for searches, regular independent audits, restrictions on external data sharing and public reporting on how often the technology helps solve crimes.

Flock’s expanding network demonstrates the scale of the market for surveillance technology. The resistance demonstrates that deployment alone cannot guarantee public legitimacy.

For American cities, the decision is no longer only whether license-plate readers work. It is whether their benefits justify the privacy risks, and what enforceable limits communities should demand before allowing the systems to expand further.

New software can detect when people text and drive

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Computer algorithms developed by engineering researchers at the University of Waterloo can accurately determine when drivers are texting or engaged in other distracting activities.

The system uses cameras and artificial intelligence (AI) to detect hand movements that deviate from normal driving behaviour and grades or classifies them in terms of possible safety threats.

Fakhri Karray, an electrical and computer engineering professor at Waterloo, said that information could be used to improve road safety by warning or alerting drivers when they are dangerously distracted. And as advanced self-driving features are increasingly added to conventional cars, he said, signs of serious driver distraction could be employed to trigger protective measures.

“The car could actually take over driving if there was imminent danger, even for a short while, in order to avoid crashes,” said Karray, a University Research Chair and director of the Centre for Pattern Analysis and Machine Intelligence (CPAMI) at Waterloo.

Algorithms at the heart of the technology were trained using machine-learning techniques to recognize actions such as texting, talking on a cellphone or reaching into the backseat to retrieve something. The seriousness of the action is assessed based on duration and other factors.

That work builds on extensive previous research at CPAMI on the recognition of signs, including frequent blinking, that drivers are in danger of falling asleep at the wheel. Head and face positioning are also important cues of distraction. Ongoing research at the centre now seeks to combine the detection, processing and grading of several different kinds of driver distraction in a single system.

“It has a huge impact on society,” said Karray, citing estimates that distracted drivers are to blame for up to 75 per cent of all traffic accidents worldwide.

Another research project at CPAMI is exploring the use of sensors to measure physiological signals such as eye-blinking rate, pupil size and heart-rate variability to help determine if a driver is paying adequate attention to the road.

Karray’s research — done in collaboration with PhD candidates Arief Koesdwiady and Chaojie Ou, and post-doctoral fellow Safaa Bedawi — was recently presented at the 14th International Conference on Image Analysis and Recognition in Montreal.