“AI Urban Management Officer” is here—An automated encroachment detection system built on OpenMV

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

Unauthorised street trading is a common issue in urban governance; trading facilities such as stalls, shelves, and tables encroach upon footpaths or roadways, disrupting traffic order and posing safety hazards—particularly during morning and evening peak 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 problems recur immediately after personnel leave.

Based on SingTown Technology’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 fixedly deployed, 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 operate beyond their licensed scope, 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 enforcement actions.

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