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

Recurring unauthorised street trading is challenging not because of “detecting it once” but because of 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 footpaths or roadways, not only disrupting traffic order but also posing safety hazards—particularly during peak morning and evening 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 high-priority areas such as key road sections, school perimeters, and commercial districts—areas prone to recurring incidents.

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 enforcement actions.

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 both the responsiveness and precision of urban governance.