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GithubUnleashed pets pose a recurring management challenge in communities and parks—visible yet difficult to regulate. Manual inspections are prone to blind spots, while conventional surveillance systems struggle to automatically detect nuanced behaviours. OpenMV enables the system to directly identify whether a pet is leashed—for the first time.

In public areas such as communities, parks, and pedestrian streets, pets being off-leash is a frequent and difficult-to-manage issue.
Manual inspections are prone to temporal and spatial blind spots, while conventional video surveillance—although capable of “seeing”—struggles to automatically determine the specific behaviour of “leash usage,” resulting in insufficient enforcement. Leveraging SingTown’s OpenMV intelligent cameras, the system employs AI-powered vision capabilities to automatically identify on-site pet leash usage status, thereby providing technical support for civilized pet ownership management.
Cameras are deployed at community entrances and exits, main passageways, or key areas to continuously capture footage.
OpenMV runs object detection algorithms on the edge device to simultaneously identify “people” and “pets” in the image, and further analyzes small targets between them. By combining leash-line feature recognition with spatial relationship modeling, it determines whether the pet is under effective restraint.
When the system detects a scenario where a person and their pet are walking together without a leash, it can instantly output the result on the device side, trigger an audio alert, or report the incident to the community management platform to enable rapid intervention.
OpenMV deploys complex visual recognition and decision-making logic directly on the camera edge device, offering advantages such as low power consumption, low latency, and no requirement for continuous internet connectivity—making it suitable for long-term operation scenarios such as communities and parks, and elevating civilized pet management from “passive persuasion” to “proactive identification.”

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