“AI Urban Management Officer” is here: an automated unauthorised street vending detection system built on OpenMV

Recurring unauthorised street trading poses a governance challenge not in “detecting it once” but in its “long-term persistence.” OpenMV enables management to shift from periodic rectification to daily perception through continuous visual monitoring.

Unauthorised street trading is a common issue in urban governance; trading facilities such as stalls, shelves, and tables encroach upon pedestrian walkways or vehicle carriageways, not only disrupting traffic order but also posing safety hazards—particularly pronounced during morning and evening peak hours or in areas with high pedestrian density.

Management relying on manual inspections incurs high labour costs and makes it difficult to achieve long-term, continuous supervision, resulting in management blind spots where problems recur immediately after personnel leave.

Based on SingTown’s OpenMV smart 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 trolleys. 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 roads, obstruct pedestrian pathways, or exceed their licensed scope of operations, it can instantly output identification results at the edge device for on-site alerts, evidence collection for violations, 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, encroachment management and regulation have shifted from periodic rectification to continuous perception and real-time detection, significantly reducing the burden on manual inspections and enhancing both the responsiveness and the precision of urban governance.