AI No-Parking Cameras Now Issuing E-Challans in Karachi, Pakistan

Karachi’s Safe City Project now uses AI to automatically fine illegally parked vehicles. The faceless e-challan system aims to reduce congestion and corruption, but raises privacy questions.

By Inside AI Editorial Team July 16, 2026
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July 16, 2026, (Inside AI) — Karachi’s streets are now under the watch of AI-powered cameras that automatically detect illegal parking and issue fines without any human officer on site. The Sindh Safe Cities Authority has activated Smart No-Parking Cameras across the city, integrating them with the existing Traffic Police TRACS system to generate faceless e-challans in real time.

This expansion leverages a network of over 1,300 AI-based cameras already deployed under the Karachi Safe City Project. These cameras, positioned at key points, use artificial intelligence to identify vehicles, individuals, and activities. The new no-parking detection capability extends automated enforcement to a common urban violation that often clogs roads and disrupts traffic flow.

Director General of the Safe Cities Authority Sarfaraz Nawaz confirmed the operational details.

“The new cameras will identify vehicles parked in no-parking zones and transmit the information directly to the Karachi Traffic Police. Owners found in violation will be issued e-challans automatically through the TRACS system, without any personnel needing to be present at the site,” Nawaz said.

The TRACS system has already been issuing faceless challans for other traffic violations, such as signal jumping and speeding, using the same camera network. Adding parking enforcement is a logical next step in digitizing traffic management. The system captures license plate data, processes it through AI algorithms, and triggers a challan that is sent to the vehicle owner’s registered address or mobile number.

Automated Enforcement and Urban Discipline

Karachi, a megacity of over 20 million, has long struggled with traffic congestion partly caused by haphazard parking. Traditional enforcement relies on traffic wardens who cannot be everywhere. Automated systems promise 24/7 coverage and remove human bias or corruption from the process.

However, similar systems in other cities have faced challenges. In London and New York, automated parking enforcement sparked debates over privacy, accuracy, and revenue motives. Critics argue that without proper signage and grace periods, such systems can feel punitive. Karachi’s authorities must ensure transparency about camera locations and a robust appeals process to maintain public trust.

Nawaz emphasized that the goal is not just penalization but behavioral change.

“The move is intended to make enforcement of traffic laws more effective and far-reaching across the city,” he said. He urged motorists to follow parking regulations and always park in designated areas, adding that the effort aims to help citizens avoid challans and inconvenience while contributing to a smarter, more disciplined Karachi.

Technical Backbone and Privacy Questions

The Safe City Project’s AI cameras are equipped with ANPR (Automatic Number Plate Recognition) technology, capable of reading plates at high speeds and in low light. Data flows to a central command center where algorithms verify violations before issuing challans. The system reportedly uses edge computing to reduce latency, allowing near-instant detection.

Privacy advocates have raised concerns about mass surveillance. The Sindh Safe Cities Authority has not publicly detailed data retention policies or who has access to the footage. In India, similar projects faced legal challenges over privacy rights, leading to stricter guidelines. Pakistan lacks comprehensive data protection laws, leaving such systems in a regulatory gray area.

Despite these concerns, the expansion signals Karachi’s ambition to become a “smart city.” The no-parking cameras are part of a broader push that includes AI-based crime detection and traffic management. Whether the technology delivers smoother roads or just more fines will depend on implementation and public acceptance.

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