“AI Urban Management Officer” is here—a system for automatic identification of unauthorised street vending, built on OpenMV

Recurring unauthorised street trading 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 everyday situational awareness.

Unauthorised street trading is a common issue in urban governance, where trading facilities such as stalls, shelves and tables encroach upon footpaths or roadways, disrupting traffic order and 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 supervision, resulting in management blind spots where issues recur immediately after personnel depart.

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 unauthorised street trading, such as stalls, shelves, sunshades and trolleys. It also determines in real time whether unauthorised trading activities have crossed boundaries by integrating road edge information, pedestrian pathway extents and configured virtual rule lines.

When the system detects that business facilities are encroaching on roads, obstructing pedestrian pathways or operating beyond their licensed scope, 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 OpenMV’s on-device AI capabilities and edge deployment advantages, the management of unauthorised street occupation 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.