“AI Urban Management Officer” Is Here: An Automatic Vendors-Encroaching-on-Pavement Recognition System Built on OpenMV

Recurring unauthorized street vending poses a governance challenge—not in “detecting it once,” but in its “persistent presence.” OpenMV enables continuous visual monitoring, shifting management from periodic rectification to daily situational awareness.

Occupying public roadways for commercial activities is a common issue in urban governance. Commercial facilities—such as stalls, shelves, and tables and chairs—encroach upon sidewalks or vehicular lanes, disrupting traffic order and posing safety hazards, particularly during morning and evening rush hours or in areas with high pedestrian density.

Managing through manual inspections incurs high labour costs and makes it difficult to achieve long-term, continuous monitoring, resulting in management blind spots where violations recur immediately after inspectors leave.

Based on SingTown’s OpenMV intelligent camera, automated and routine visual monitoring can be conducted in areas prone to recurring incidents, such as key road sections, school perimeters, and commercial districts.

After the camera is fixed in place, OpenMV runs object detection algorithms on-device to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and carts. It also determines in real time whether encroachment has occurred by integrating road boundaries, pedestrian walkway extents, and configured virtual rule lines.

When the system detects that business facilities encroach upon roadways, obstruct pedestrian pathways, or exceed the scope of licensed business operations, it can instantly output identification results at the edge device for on-site alerts, violation evidence collection, or automatic reporting to the urban management platform, thereby providing objective evidence for subsequent law enforcement.

Leveraging the on-device AI and edge deployment advantages of OpenMV, the management of unauthorized occupation of public space has shifted from periodic rectification to continuous perception and real-time detection, significantly reducing the burden on manual inspections and enhancing both the responsiveness and precision of urban governance.