“AI Municipal Supervisor” is here—An automated encroachment detection system built on OpenMV

Recurring illegal street vending is a persistent challenge—not in detecting individual instances, but in addressing its long-term presence. OpenMV enables continuous visual monitoring, shifting management from periodic rectification to daily situational awareness.

Street vending is a common issue in urban governance, where vending stalls, shelves, tables, and chairs encroach upon pedestrian walkways or vehicular lanes, not only disrupting traffic order but also 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 supervision, resulting in management blind spots where violations resume immediately after inspectors 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 fixedly deployed, OpenMV runs object detection algorithms on the edge device to automatically identify objects related to encroachment on public space, such as stalls, shelves, sunshades, and trolleys. Simultaneously, by integrating road boundaries, pedestrian passage areas, and configured virtual rule lines, it determines in real time whether any commercial activity has crossed the designated boundaries.

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 law enforcement.

Leveraging the on-device AI and edge deployment advantages of OpenMV, encroachment management 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.